Systems and methods relating to customer experience automation

ABSTRACT

A method for personalizing a delivery of services to a first customer including: providing a customer profile; updating the customer profile via performing a first process to collect interaction data, the first process including the steps of: monitoring activity on the communication device and, therefrom, detecting the first interaction with the first contact center; identifying data relating to the first interaction for collecting as the interaction data; and updating the customer profile to include the interaction data identified from the first interaction; generating an interaction predictor, the interaction predictor comprising knowledge about the first customer derived, at least in part, from the data stored within the customer profile, the knowledge comprising a behavioral factor attributable to the first customer given a first type of interaction; and augmenting the customer profile by storing therein the interaction predictor.

BACKGROUND

The present invention generally relates to telecommunications systems inthe field of customer relations management including customer assistancevia internet-based service options. More particularly, but not by way oflimitation, the present invention pertains to systems and methods forautomating the customer experience, including aspects of customerservice offered through an application executed on a mobile computingdevice.

BRIEF DESCRIPTION OF THE INVENTION

The present invention may include a computer-implemented method forpersonalizing a delivery of services to a first customer, the firstcustomer having a communication device through which the first customerconducts interactions with contact centers. The method may include thefollowing steps: providing a customer profile that stores data relatedto the first customer; updating the customer profile via performing afirst process to collect interaction data related to the interactionsbetween the first customer and the contact centers, the first processbeing performed repetitively so to update the customer profile aftereach successive one of the interactions, wherein, described in relationto an exemplary first one of the interactions (hereinafter “firstinteraction”) between the first customer and a first one of the contactcenters (hereinafter “first contact center”), the first process includesthe steps of: monitoring activity on the communication device and,therefrom, detecting the first interaction with the first contactcenter; identifying data relating to the first interaction forcollecting as the interaction data; and updating the customer profile toinclude the interaction data identified from the first interaction;generating an interaction predictor, the interaction predictorcomprising knowledge about the first customer derived, at least in part,from the data stored within the customer profile, the knowledgecomprising a behavioral factor attributable to the first customer givena first type of interaction (hereinafter “first interaction type”); andaugmenting the customer profile by storing therein the interactionpredictor, the storage of the interaction predictor linking thebehavioral factor to the first interaction type to facilitate real timeretrieval when detecting an incoming interaction of the firstinteraction type.

These and other features of the present application will become moreapparent upon review of the following detailed description of theexample embodiments when taken in conjunction with the drawings and theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention, and many of theattendant features and aspects thereof, will become more readilyapparent as the invention becomes better understood by reference to thefollowing detailed description when considered in conjunction with theaccompanying drawings in which like reference symbols indicate likecomponents, wherein:

FIG. 1 depicts a schematic block diagram of a computing device inaccordance with exemplary embodiments of the present invention and/orwith which exemplary embodiments of the present invention may be enabledor practiced;

FIG. 2 depicts a schematic block diagram of a communicationsinfrastructure or contact center in accordance with exemplaryembodiments of the present invention and/or with which exemplaryembodiments of the present invention may be enabled or practiced;

FIG. 3 is schematic block diagram showing further details of a chatserver operating as part of the chat system according to embodiments ofthe present invention;

FIG. 4 is a schematic block diagram of a chat module according toembodiments of the present invention;

FIG. 5 is an exemplary customer chat interface according to embodimentsof the present invention;

FIG. 6 is a block diagram of a customer automation system according toembodiments of the present invention;

FIG. 7 is a flowchart of a method for automating an interaction onbehalf of a customer according to embodiments of the present invention;

FIG. 8 is a block diagram of an automated personal bot for a customeraccording to embodiments of the present invention;

FIG. 9 is an example as to how a customer interaction is processedaccording to embodiments of the present invention;

FIG. 10 is an example as to how a customer interaction is processedaccording to embodiments of the present invention;

FIG. 11 is an example as to how a customer interaction is processedaccording to embodiments of the present invention;

FIG. 12 is a schematic representation of an exemplary system including apersonal bot and personalized customer profile in accordance with thepresent invention;

FIG. 13 is a process related to personalized customer profile inaccordance with the present invention;

FIG. 14 is a schematic representation of an automated routing system inaccordance with the present invention;

FIG. 15 is a routing process in accordance with the present invention;and

FIG. 16 is a routing process in accordance with the present invention.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the exemplary embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will be apparent, however, to one having ordinaryskill in the art that the detailed material provided in the examples maynot be needed to practice the present invention. In other instances,well-known materials or methods have not been described in detail inorder to avoid obscuring the present invention. Additionally, furthermodification in the provided examples or application of the principlesof the invention, as presented herein, are contemplated as wouldnormally occur to those skilled in the art.

As used herein, language designating nonlimiting examples andillustrations includes “e.g.”, “i.e.”, “for example”, “for instance” andthe like. Further, reference throughout this specification to “anembodiment”, “one embodiment”, “present embodiments”, “exemplaryembodiments”, “certain embodiments” and the like means that a particularfeature, structure or characteristic described in connection with thegiven example may be included in at least one embodiment of the presentinvention. Thus, appearances of the phrases “an embodiment”, “oneembodiment”, “present embodiments”, “exemplary embodiments”, “certainembodiments” and the like are not necessarily all referring to the sameembodiment or example. Further, particular features, structures orcharacteristics may be combined in any suitable combinations and/orsub-combinations in one or more embodiments or examples.

Embodiments of the present invention may be implemented as an apparatus,method, or computer program product. Accordingly, example embodimentsmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.), or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “module” or “system.” Further,example embodiments may take the form of a computer program productembodied in any tangible medium of expression having computer-usableprogram code embodied in the medium. In addition, it will be appreciatedthat the figures provided herewith are for explanation purposes topersons ordinarily skilled in the art and that the drawings are notnecessarily drawn to scale.

It will be further appreciated that the flowchart and block diagramsprovided in the figures illustrate architecture, functionality, andoperation of possible implementations of systems, methods, and computerprogram products according to example embodiments of the presentinvention. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of code, which comprises oneor more executable instructions for implementing the specified logicalfunctions. It will also be noted that each block of the block diagramsand/or flowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustrations, may be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions. These computer program instructions may also be stored ina computer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

Exemplary Computing Device

Turning now to FIG. 1, a schematic block diagram of an exemplarycomputing device 100 is shown in accordance with embodiments of thepresent invention and/or with which exemplary embodiments of the presentinvention may be enabled or practiced. It should be appreciated thatFIG. 1 is provided as a non-limiting example.

The computing device 100, as used herein, may be implemented viafirmware (e.g., an application-specific integrated circuit), hardware,or a combination of software, firmware, and hardware. It will beappreciated that each of the servers, controllers, switches, gateways,engines, and/or modules in the following figures (which collectively maybe referred to as servers) may be implemented via one or more of thecomputing devices 100. For example, the various servers may be a processor thread running on one or more processors of one or more computingdevices 100 executing computer program instructions and interacting withother system components for performing the various functionalitiesdescribed herein. A server also may be a software module, for example, asoftware module of the contact center system 200 depicted in FIG. 2 mayinclude one or more servers. Further, unless otherwise specificallylimited, the functionality described in relation to a plurality ofcomputing devices may be integrated into a single computing device 100,or the functionality described in relation to a single computing devicemay be distributed across several computing devices 100. In relation tocomputing systems described herein, such as the contact center system200 of FIG. 2, the various servers and computer systems thereof may belocated on one or more local computing devices 100 (i.e., on-site at thesame physical location as the agents of the contact center) or may belocated on one or more remote computing devices 100 (i.e., off-site orin the cloud in a geographically different location, for example, in aremote data center connected to the contact center via a network). Inexemplary embodiments, functionality provided by servers located oncomputing devices off-site may be accessed and provided over a virtualprivate network (VPN) as if such servers were on-site, or thefunctionality may be provided using a software as a service (SaaS) toprovide functionality over the Internet using various protocols, such asby exchanging data using encoded in extensible markup language (XML) orJSON.

Though other configurations are also possible, in the illustratedexample, the computing device 100 include a central processing unit(CPU) or processor 105 and a main memory 110. The computing device 100also includes a storage device 115, a removable media interface 120, anetwork interface 115, one or more input/output (I/O) devices 135, whichas depicted includes an I/O controller 130, a display device 135A, akeyboard 135B, and a pointing device 135C (e.g., a mouse). The storagedevice 115 may provide storage for an operating system and software runon the computing device. The computing device 100 further includesadditional optional elements, such as a memory port 140, a bridge 145,one or more additional input/output devices 135D, 135E, 135F, and acache memory 150 in communication with the processor 105.

The processor 105 of the computing device 100 may be any logic circuitrythat responds to and processes instructions fetched from the main memory110. It may be implemented, for example, in an integrated circuit, inthe form of a microprocessor, microcontroller, or graphics processingunit, or in a field-programmable gate array (FPGA) orapplication-specific integrated circuit (ASIC). The main memory 110 maybe one or more memory chips capable of storing data and allowing anystorage location to be directly accessed by the central processing unit105. Though other configurations are possible, as shown in theillustrated example, the central processing unit 105 may communicatedirectly with the main memory 110 via a memory port 140 and indirectlywith the storage device 115 via a system bus 155.

In exemplary embodiments, the processor 105 may include a plurality ofprocessors and may provide functionality for simultaneous execution ofinstructions or for simultaneous execution of one instruction on morethan one piece of data. The computing device 100 may include a parallelprocessor with one or more cores. The computing device 100 may include ashared memory parallel device, with multiple processors and/or multipleprocessor cores, accessing all available memory as a single globaladdress space. In another embodiment, the computing device 100 may be adistributed memory parallel device with multiple processors eachaccessing local memory only. The computing device 100 may have both somememory which is shared and some which may only be accessed by particularprocessors. The processor 105 may include a multicore microprocessor,which combines two or more independent processors into a single package,e.g., into a single integrated circuit (IC). In exemplary embodiments,the processor 105 may provide single instruction multiple data (SIMD)functionality. In another embodiment, several processors in theprocessor 105 may provide functionality for execution of multipleinstructions simultaneously on multiple pieces of data (MIMD).

As depicted in FIG. 1, the processor 105 may communicate directly withthe cache memory 150 via a secondary bus or backside bus. In otherembodiments, the processor 105 communicates with the cache memory 150using the system bus 155. The cache memory 150 typically has a fasterresponse time than main memory 110. As illustrated, the processor 105may communicate with various I/O devices 135 via the local system bus155, though direct communication though backside buses are alsopossible. Various buses may be used as the local system bus 155 inaccordance with conventional technology. For embodiments in which an I/Odevice is a display device 135A, the processor 105 may communicate withthe display device 135A through an advanced graphics port (AGP).

A wide variety of I/O devices 135 may be present in the computing device100. Input devices may include one or more keyboards 135, mice,trackpads, trackballs, microphones, and drawing tablets, to name a fewnon-limiting examples. Output devices may include video display devices,speakers and printers. An I/O controller 130 may be used to control theI/O devices, such as, for example, as a keyboard 135B and a pointingdevice 135C (e.g., a mouse or optical pen).

The computing device 100 may support one or more removable mediainterfaces 120, such as a floppy disk drive, a CD-ROM drive, a DVD-ROMdrive, tape drives of various formats, a USB port, or any other devicesuitable for reading data from read-only media, or for reading datafrom, or writing data to, read-write media. The removable mediainterface 120, for example, may be used for installing software andprograms. The computing device 100 may further include a storage device115, such as one or more hard disk drives or hard disk drive arrays, forstoring an operating system and other related software. Optionally, aremovable media interface 120 may also be used as the storage device.

The computing device 100 may include or be connected to multiple displaydevices 135A. As such, any of the I/O devices 135 and/or the I/Ocontroller 130 may include any type and/or form of suitable hardware,software, or combination of hardware and software to support, enable orprovide for the connection to, and use of, the multiple display devices135A by the computing device 100. For example, the computing device 100may include any type and/or form of video adapter, video card, driver,and/or library to interface, communicate, connect or otherwise use themultiple display devices 135A. In exemplary embodiments, a video adaptermay include multiple connectors to interface to multiple display devices135A. In another embodiment, the computing device 100 may includemultiple video adapters, with each video adapter connected to one ormore of the display devices 135A. In other embodiments, one or more ofthe display devices 135A may be provided by one or more other computingdevices, connected, for example, to the computing device 100 via anetwork. These embodiments may include any type of software designed andconstructed to use the display device of another computing device as asecond display device 135A for the computing device 100. One of ordinaryskill in the art will recognize and appreciate the various ways andembodiments that a computing device 100 may be configured to havemultiple display devices 135A.

The computing device 100 may operate under the control of an operatingsystem, which controls scheduling of tasks and access to systemresources. The computing device 100 may run any operating system,embedded operating system, real-time operating system, open sourceoperation system, proprietary operating system, mobile device operatingsystem, or any other operating system capable of running on a computingdevice and performing the operations described herein. The computingdevice 100 may be any workstation, desktop computer, laptop or notebookcomputer, server machine, handled computer, mobile telephone, smartphone, portable telecommunication device, media playing device, gamingsystem, mobile computing device, or any other type and/or form ofcomputing, telecommunications or media device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein. In exemplaryembodiments, the computing device 100 may have different processors,operating systems, and input devices consistent with the device. Incertain embodiments, the computing device 100 is a mobile device. Inexemplary embodiments, the computing device 100 may include acombination of devices, such as a mobile phone combined with a digitalaudio player or portable media player.

The computing device 100 may be one of a plurality of machines connectedby a network, or it may include a plurality of machines so connected. Anetwork environment may include one or more local machine(s), client(s),client node(s), client machine(s), client computer(s), client device(s),endpoint(s), or endpoint node(s) in communication with one or moreremote machines (which may also be generally referred to as servermachines or remote machines) via one or more networks. In exemplaryembodiments, a local machine has the capacity to function as both aclient node seeking access to resources provided by a server machine andas a server machine providing access to hosted resources for otherclients. The network may be LAN or WAN links, broadband connections,wireless connections, or a combination of any or all of the above.Connections may be established using a variety of communicationprotocols. In one embodiment, the computing device 100 communicates withother computing devices 100 via any type and/or form of gateway ortunneling protocol such as secure socket layer (SSL) or transport layersecurity (TLS). The network interface may include a built-in networkadapter, such as a network interface card, suitable for interfacing thecomputing device to any type of network capable of communication andperforming the operations described herein. As discussed more below,aspects of the computing device 100 may include components, serves, orother modules that are a cloud-based or implemented within a cloudcomputing environment

In exemplary embodiments, a network environment may be a virtual networkenvironment where the various network components are virtualized. Forexample, the various machines may be virtual machines implemented as asoftware-based computer running on a physical machine. The virtualmachines may share the same operating system, or, in other embodiments,different operating system may be run on each virtual machine instance.In exemplary embodiments, a “hypervisor” type of virtualizing is usedwhere multiple virtual machines run on the same host physical machine,each acting as if it has its own dedicated box. The virtual machines mayalso run on different host physical machines. Other types ofvirtualization are also contemplated, such as, for example, the network(e.g., via software defined networking (SDN)). Functions, such asfunctions of a session border controller, may also be virtualized, suchas, for example, via network functions virtualization (NFV).

Contact Centers

With reference now to FIG. 2, a communications infrastructure or, asused herein, contact center system 200 is shown in accordance withexemplary embodiments of the present invention and/or with whichexemplary embodiments of the present invention may be enabled orpracticed. As used therein, the term “contact center system” is used torefer to the system depicted in FIG. 2 and/or the components thereof,while the term “contact center” is used more generally to refer tocontact center systems, customer service providers, and/or theorganizations or enterprises associated therewith. Thus, unlessotherwise limited, the term “contact center” refers generally to acontact center system (such as the contact center system 200), theassociated customer service provider (such as a particular customerservice provider providing customer services through the contact centersystem 200), as well as the organization or enterprise on behalf ofwhich those customer services are being provided.

By way of background, customer service providers generally offer manytypes of services through contact centers. Such contact centers may bestaffed with employees and/or customer service agents (or simply“agents”), with the agents serving as an interface between anorganization, such as a company, enterprise, or government agency, andpersons, such as users or customers (hereinafter generally referred toas “customers”). For example, the agents at a contact center may assistcustomers in making purchasing decisions and receive purchase orders.Similarly, agents may assist or support customers in solving problemswith products or services already provided by the organization. Within acontact center, such interactions between contact center agents andoutside entities or customers may be conducted over a variety ofcommunication channels, such as, for example, via voice (e.g., telephonecalls or voice over IP or VoIP calls), video (e.g., video conferencing),text (e.g., emails and text chat), or through other media.

Operationally, contact centers generally strive to provide qualityservices to customers, while minimizing costs. For example, one way fora contact center to operate is to handle every customer interaction witha live agent. While this approach may score well in terms of the servicequality, it likely would also be prohibitively expensive due to the highcost of agent labor. Because of this, most contact centers utilize somelevel of automated processes in place of live agents, such as, forexample, interactive voice response (IVR) systems, interactive mediaresponse (IMR) systems, internet robots or “bots”, automated chatmodules or “chatbots”, and the like. In many cases this has proven to bea successful strategy, as automated processes can be highly efficient inhandling certain types of interactions and effective at decreasing theneed for live agents. Such automation allows contact centers to targetthe use of human agents for the more difficult customer interactions,while the automated processes handle the more repetitive or routinetasks. Further, automated processes can be structured in a way thatoptimizes efficiency and promotes repeatability. Whereas a human or liveagent may forget to ask certain questions or follow-up on particulardetails, such mistakes are typically avoided through the use ofautomated processes. As a result, customer service providers areincreasingly relying on automated processes to interact with customers.

However, while such automation technology is now commonly used bycontact centers to increase efficiency, it remains far less developedfor use by customers. Thus, while IVR systems, IMR systems, and/or botsare used to automate portions of the interaction on the contact centerside of the interaction, the actions on the customer-side are still leftfor the customer to perform manually. As will be seen, embodiments ofthe present invention relate to systems and methods for automatingaspects of the customer-side of the interactions between customers andcustomer service providers or contact centers. Accordingly, presentembodiments may provide ways to automate actions that customers arerequired to perform when contacting and interacting with customerservice providers or contact centers. For example, embodiments of thepresent invention include methods and systems for identifyingoutstanding matters or pending actions for a customer that needadditional attention stemming from a previous interaction between thecustomer and a contact center. Once identified, other embodiments mayinclude methods and systems for automating follow-up actions on behalfof the customer for resolving such pending actions.

Referring specifically to FIG. 2, a block diagram is presented thatillustrates an embodiment of a communications infrastructure or contactcenter system 200 in accordance with the present invention and/or anenvironment within which embodiments of the present invention may beenabled or practiced. The contact center system 200 may be used by acustomer service provider to provide various types of services tocustomers. For example, the contact center system 200 may be used toengage and manage chat conversations in which automated chat robots orbots and/or human agents communicate with customers. As will beappreciated, the contact center system 200 may be used as an in-housefacility to a business or enterprise for serving the enterprise inperforming the functions of sales and service relative to the productsand services available through the enterprise. In another aspect, thecontact center system 200 may be operated by a third-party serviceprovider. According to another embodiment, the contact center system 200may operate as a hybrid system in which some components are hosted atthe contact center premise while other components are hosted remotely(e.g., in a cloud-based or cloud computing environment). The contactcenter system 200 may be deployed on equipment dedicated to theenterprise or third-party service provider, and/or deployed in a remotecomputing environment such as, for example, a private or public cloudenvironment with infrastructure for supporting multiple contact centersfor multiple enterprises. As discussed more below, the contact centersystem 200 may include software applications or programs, which may beexecuted on premises or remotely or some combination thereof. It shouldfurther be appreciated that the various components of the contact centersystem 200 may also be distributed across various geographic locationsand computing environments and not necessarily contained in a singlelocation, computing environment, or even computing device.

Further, it should be generally noted that, unless otherwisespecifically limited, any of the computing elements of present inventionmay be implemented in cloud-based or cloud computing environments. Asused herein, “cloud computing” may be defined as a model for enablingubiquitous, convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, and services) that can be rapidly provisioned viavirtualization and released with minimal management effort or serviceprovider interaction, and then scaled accordingly. Cloud computing canbe composed of various characteristics (e.g., on-demand self-service,broad network access, resource pooling, rapid elasticity, measuredservice, etc.), service models (e.g., Software as a Service (“SaaS”),Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”),and deployment models (e.g., private cloud, community cloud, publiccloud, hybrid cloud, etc.). Also often referred to as a “serverlessarchitecture”, a cloud computing (or simply “cloud”) execution modelgenerally includes a service provider dynamically managing an allocationand provisioning of remote servers for achieving a desiredfunctionality. It will be appreciated that such “serverless” platformsstill require servers.

In accordance with the exemplary embodiment of FIG. 2, the components ormodules of the contact center system 200 may include: a plurality ofcustomer devices 205A, 205B, 205C; a communications network 210 (alsoreferred to simply as network 210); a switch/media gateway 212; a callcontroller 214; an interactive media response (IMR) server 216; arouting server 218; a storage device 220; a statistics or stat server226; a plurality of agent devices 230A, 230B, 230C that include workbins232A, 232B, 232C, respectively; a multimedia/social media server 234; aknowledge management server 234 coupled to a knowledge system 238; achat server 240, web servers 242; an interaction (iXn) server 244; auniversal contact server (UCS) 246; a reporting server 248; mediaservices 249; and an analytics module 250. As will be seen, the contactcenter system 200 manages resources (e.g., personnel, computers,telecommunication equipment, etc.) to enable delivery of services viatelephone, email, chat, or other communication mechanisms. Such servicesmay vary depending on the type of contact center and range from customerservice to help desk, emergency response, telemarketing, order taking,etc.

For example, in accordance with an embodiment, customers desiring toreceive services from the contact center system 200 may initiate inboundcommunications (e.g., telephone calls, emails, chats, etc.) to thecontact center system 200 via a customer device 205. While FIG. 2 showsthree such customer devices—i.e., customer devices 205A, 205B, and205C—it should be understood that any number may be present. Each of thecustomer devices 205 may be a communication device conventional in theart, such as a telephone, wireless phone, smart phone, personalcomputer, electronic tablet, or laptop, to name some non-limitingexamples. In general, the customer devices 205 are used by customers toinitiate, manage, and respond to telephone calls, emails, chats, textmessages, web-browsing sessions, and other multi-media transactions inaccordance with any of the functionality described herein. For example,a customer may use a customer device 205 to contact the contact centersystem 200 by way of a chat channel with the text being transmitted to achatbot or human agent. A response from the chatbot or human agent maybe generated and delivered to the customer device 205 as text.

Inbound and outbound communications from and to the customer devices 205may traverse the network 210, with the nature of network depending onthe type of customer device being used and form of communication. As anexample, the network 210 may include a communication network oftelephone, cellular, and/or data services and may also comprise aprivate or public switched telephone network (PSTN), local area network(LAN), private wide area network (WAN), and/or public WAN such as theInternet. The network 210 may also include a wireless carrier networkincluding a code division multiple access (CDMA) network, global systemfor mobile communications (GSM) network, or any wirelessnetwork/technology conventional in the art, including but not limited to3G, 4G, LTE, etc.

Embodiments of the contact center system 200 may include a switch/mediagateway 212 coupled to the network 210 for receiving and transmittingtelephone calls between the customers and the contact center system 200.The switch/media gateway 212 may include a telephone switch orcommunication switch configured to function as a central switch foragent level routing within the center. The switch may be a hardwareswitching system or a soft switch implemented via software. For example,the switch 215 may include an automatic call distributor, a privatebranch exchange (PBX), an IP-based software switch, and/or any otherswitch with specialized hardware and software configured to receiveInternet-sourced interactions and/or telephone network-sourcedinteractions from a customer, and route those interactions to, forexample, an agent telephone or communication device. In this example,the switch/media gateway establishes a voice path/connection between thecalling customer and the agent telephone device, by establishing, forexample, a connection between the customer's telephone device and theagent telephone device.

In exemplary embodiments, the switch is coupled to a call controller 214which, for example, serves as an adapter or interface between the switchand the remainder of the routing, monitoring, and othercommunication-handling components of the contact center. The callcontroller 214 may be configured to process PSTN calls, VoIP calls, etc.For example, the call controller 214 may include computer-telephoneintegration (CTI) software for interfacing with the switch/media gatewayand contact center equipment. In exemplary embodiments, the callcontroller 214 may include a session initiation protocol (SIP) serverfor processing SIP calls. The call controller 214 may also extract dataabout the customer interaction, such as the caller's telephone number(e.g., the automatic number identification (ANI) number), the customer'sinternet protocol (IP) address, or email address, and communicate withother components of the contact center system 200 in processing theinteraction.

Embodiments of the contact center system 200 may include an interactivemedia response (IMR) server 216. The IMR server 216 may also be referredto as a self-help system, a virtual assistant, etc. The IMR server 216may be similar to an interactive voice response (IVR) server, exceptthat the IMR server 216 is not restricted to voice and additionally maycover a variety of media channels. In an example illustrating voice, theIMR server 216 may be configured with an IMR script for queryingcustomers on their needs. For example, a contact center for a bank maytell customers via the IMR script to ‘press 1’ if they wish to retrievetheir account balance. Through continued interaction with the IMR server216, customers may be able to complete service without needing to speakwith an agent. The IMR server 216 may also ask an open-ended questionsuch as, “How can I help you?” and the customer may speak or otherwiseenter a reason for contacting the contact center. The customer'sresponse may be used by a routing server 218 to route the call orcommunication to an appropriate contact center system 200 resource.

For example, if the communication is to be routed to an agent, the callcontroller 214 may interact with the routing server (also referred to asan orchestration server) 218 to find an appropriate agent for processingthe interaction with the particular customer. The selection of anappropriate agent for routing an inbound customer interaction may bebased, for example, on a routing strategy employed by the routing server218, and further based on stored information about the customer andagents (which, as described more below, may be maintained in customerand agent databases on the storage device 220) and other routingparameters provided, for example, by the statistics server 226, whichaggregates data relating to the performance of the contact center system200. The routing server 218, for example, may query such data via anANI. Thus, in general, the routing server 218 may query data relevant toan incoming interaction for facilitating the routing of that interactionto the most appropriate contact center.

Regarding data storage, the contact center system 200 may include one ormore mass storage devices—represented generally by the storage device220—that stores one or more databases of data deemed relevant to thefunctioning of the contact center system 200. For example, the storagedevice 220 may store customer data that is maintained in a customerdatabase (also CDB) 222. Customer data maintained by the contact centersystem 200 may include customer profiles, contact information, servicelevel agreement (SLA), and interaction history (e.g., details of eachprevious interaction with a customer, including nature of previouscustomer contacts, reason for the interaction, disposition data, waittime, handle time, and actions taken by the contact center to resolvecustomer issues). As another example, the storage device 220 may storeagent data in an agent database (also ADB) 223. Agent data maintained bythe contact center system 200 may include agent availability, profiles,schedules, skills, etc. As another example, the storage device 220 maystore interaction data in an interaction database (also IDB) 224.Interaction data may include data relating to numerous past interactionsbetween customers and contact centers. More generally, it should beunderstood that, unless otherwise specified, the storage device 220 isconfigured to include databases and/or store data related to any of thetypes of information described herein, with those databases and/or databeing accessible to the other modules or servers of the contact centersystem 200 in ways that facilitate the functionality described herein.For example, the servers or modules of the contact center system 200 mayquery the databases for retrieving particular data stored therewithin aswell as transfer data to the databases for storage thereon. The storagedevice 220, for example, may take the form of a hard disk, disk array,or any other storage medium as is conventional in the art. The storagedevice 220 may be included as part of the contact center system 200 oroperated remotely by a third party. The databases, for example, may beCassandra or any NoSQL database. The databases may also be a SQLdatabase and be managed by any database management system, such as, forexample, Oracle, IBM DB2, Microsoft SQL server, Microsoft Access,PostgreSQL, etc., to name a few non-limiting examples.

In exemplary embodiments, the agent devices 230 are configured tointeract with the various components and modules of the contact centersystem 200 in ways that facilitate the functionality described herein.For example, the agent devices 230 may include a telephone adapted forregular telephone calls, VoIP calls, etc. The agent device 230 mayfurther include a computer for communicating with one or more servers ofthe contact center system 200 and performing data processing associatedwith contact center operations, as well as for interfacing withcustomers via voice and other multimedia communication mechanismspursuant to described functionality. While FIG. 2 shows three such agentdevices—i.e., agent devices 230A, 230B and 230C—it should be understoodthat any number may be present.

Once it is determined that an inbound communication should be handled bya human agent, functionality within the routing server 218 may select anagent from those available for routing the communication thereto. Asalready discussed, this selection may be based on which agent is bestsuited for handling the inbound communication. Once the appropriateagent is selected, the contact center system 200 forms a connectionbetween the customer device 205 and the agent device 230 thatcorresponds to the selected agent. As part of this connection,information about the customer and/or the customer's history may beprovided to the selected agent via his/her agent device 230. Thisinformation generally includes data that may aid the selected agent tobetter service the customer.

According to an embodiment, the contact center system 200 may include amultimedia/social media server 234 for engaging in media interactionsother than voice interactions with the customer devices 205 and/or webservers 242. The media interactions may be related, for example, toemail, vmail (voice mail through email), chat, video, text-messaging,web, social media, co-browsing, etc. The multi-media/social media server234 may take the form of any IP router conventional in the art withspecialized hardware and software for receiving, processing, andforwarding multi-media events.

Embodiments of the contact center system 200 may include a knowledgemanagement server 234 for facilitating interactions between customersoperating the customer devices 205 and a knowledge system 238. Theknowledge system 238 may be included as part of the contact centersystem 200 or operated remotely by a third party. In general, theknowledge system 238 may be a computer system capable of receivingquestions or queries and providing answers in response. The knowledgesystem 238 may include an artificially intelligent computer systemcapable of answering questions posed in natural language by retrievinginformation from information sources such as encyclopedias,dictionaries, newswire articles, literary works, or other documentssubmitted to the knowledge system 238 as reference materials, as isknown in the art. As an example, the knowledge system 238 may beembodied as IBM Watson®, though other types of systems also may be used.Additional details of the knowledge management server and knowledgesystem are provided in U.S. application Ser. No. 14/449,018, filed onJul. 31, 2014, entitled “System and Method for Controlled KnowledgeSystem Management,” the content of which is incorporated herein byreference.

According to an embodiment, the contact center system 200 may include achat server 240 for conducting and managing electronic chatcommunications with customers operating customer devices 205. As will beseen, chat communications may be conducted by the chat server 240 insuch a way that a customer communicates with both automated systems,which may also be referred to as chatbots, as well as human agents,which may also be referred to simply as agents. According to anembodiment, the chat server 240 may be configured to implement andmaintain chat conversations, generate chat transcripts, and determinewhether a chat communication is completed (e.g., based on timeout or bya customer closing a chat window). In exemplary embodiments, the chatserver 240 may also operate as a chat orchestration server, dispatchingactual chat conversations among the chatbots or available human agents.The processing logic of the chat server 240 may be rules driven, andleverage, for example, intelligent workload distribution protocols andvarious business rules for routing communications. The chat server 240further may implement, manage and facilitate user interfaces (also UIs)associated with the chat feature, including those UIs generated ateither the customer device 205 or the agent device 230. Further, thechat server 240 may orchestrate and implement chats conducted by bothhuman agents and automated chatbots. According to an embodiment, thechat server 240 is configured to transfer chats within a single chatsession with a particular customer between automated and human sourcessuch that, for example, a chat session transfers from a chatbot to ahuman agent or from a human agent to a chatbot.

The chat server 240 may also be coupled to the knowledge managementserver 234 and the knowledge systems 238 for receiving suggestions andanswers to queries posed by customers during an automated chat,providing links to knowledge articles, or the like. Additionally, thechat server 240 may be configured to facilitate (e.g., supervise andcoordinate) self-learning by certain of the chatbots. For example, priorto characteristics of individual chatbots being modified, the chatserver 240 may determine whether the feedback from customer thatprecipitated the modification is suspicious or malicious (e.g., bysearching for or identifying key words or phrases, and/or flaggingpotential issues for review by an agent). Although the chat server 240is depicted in the embodiment of FIG. 2 as being a separate servercomponent, a person of skill in the art should recognize thatfunctionalities of the chat server 240 may be incorporated into otherservers, such as, for example, the multimedia/social media server 234 orthe IMR server 216.

According to an embodiment, the web servers 242 may include socialinteraction site hosts for a variety of known social interaction sitesto which a customer may subscribe, such as Facebook, Twitter, Instagram,etc., to name a few non-limiting examples. In exemplary embodiments,although web servers 242 are depicted as part of the contact centersystem 200, the web servers 242 may also be provided by third partiesand/or maintained outside of the contact center premise. The web servers242 may also provide web pages for the enterprise that is beingsupported by the contact center system 200. Customers may browse the webpages and get information about the enterprise's products and services.

The web pages may also provide a mechanism for contacting the contactcenter via, for example, web chat, voice call, email, web real-timecommunication (WebRTC), etc. For example, widgets may be deployed on thewebsites hosted on the web servers 242. As used herein, a widget refersto a user interface component that performs some particular function. Insome implementations, a widget may include a graphical user interfacecontrol that can be overlaid on a web page displayed on the Internet.The widget may show information, such as in a window or text box, and/orinclude buttons or other controls that allow the customer to accesscertain functionalities such as sharing or opening a file. In someimplementations, a widget is a common looking user interface componenthaving a portable portion of code that can be installed and executedwithin a separate web-based page without compilation. Some componentscan include corresponding and/or additional user interfaces and canaccess a variety of resources such as local resources (e.g., a calendar,contact information, etc. on the customer device) and/or remote networkresources (e.g., instant messaging, electronic mail, social networkingupdates, etc.).

In addition, embodiments of the contact center system 200 may beconfigured to manage deferrable interactions or activities (alsoreferenced simply as deferrable activities) and the routing thereof tohuman agents for completion. As should be understood, deferrableactivities include back-office work that can be performed off-line,examples of which include responding to emails, letters, attendingtraining, and other activities that do not entail real-timecommunication with a customer. To do this, the interaction (iXn) server244 is configured to interact with the routing server 218 for selectingan appropriate agent to handle each of the deferable activities. Onceassigned to a particular agent, the deferable activity is pushed to thatagent, for example, appearing on the agent device 230 of the selectedagent. As an example, the deferable activity appear in a workbin 232 asa task for the selected agent to complete. The functionality of theworkbin 232 may be implemented via any conventional data structure, suchas, for example, a linked list, array, etc. Each of the agent devices230 may include a workbin 232, thus, workbins 232A, 232B, and 232C maybe maintained in the agent devices 230A, 230B, and 230C, respectively.As an example, a workbin 232 may be maintained in the buffer memory ofthe corresponding agent device 230.

According to an embodiment, the contact center system 200 may include auniversal contact server (UCS) 246, which is configured to retrieveinformation stored in the customer database 222 and direct informationfor storage therein. For example, the UCS 246 may be utilized as part ofthe chat feature to facilitate maintaining a history on how well chatsfor a particular customer were handled, which then may be used as areference for future chat communications. The UCS 246 also may beconfigured to facilitate maintaining a history of customers' preferencesregarding media channels, such as instances in which chat communicationsare acceptable and instances in which customers prefer alternate mediachannels. Additionally, the UCS 246 may be configured to record aninteraction history for each customer, capturing and storing dataregarding comments from agents, customer communication history, and thelike. Each of these data types may be stored on the customer database222 or on other modules as described functionality requires.

Example embodiments of the contact center system 200 may further includea reporting server 248 configured to generate reports from dataaggregated by the statistics server 226. Such reports may include nearreal-time reports or historical reports concerning the state ofresources, such as, for example, average wait time, abandonment rate,agent occupancy, etc. The reports may be generated automatically or inresponse to specific requests from a requestor (e.g., agent,administrator, contact center application, etc.).

According to an embodiment, the media services 249 may provide audioand/or video services to support contact center features such as promptsfor an IVR or IMR system (e.g., playback of audio files), hold music,voicemails/single party recordings, multi-party recordings (e.g., ofaudio and/or video calls), speech recognition, dual tone multi frequency(DTMF) recognition, faxes, audio and video transcoding, secure real-timetransport protocol (SRTP), audio conferencing, video conferencing,coaching (e.g., support for a coach to listen in on an interactionbetween a customer and an agent and for the coach to provide comments tothe agent without the customer hearing the comments), call analysis, andkeyword spotting.

According to an embodiment, the analytics module 250 may provide systemsand methods for performing analytics on interaction data from aplurality of different data sources such as different applicationsassociated with a contact center or an organization. Aspects ofembodiments of the present invention are also directed to generating,updating, training, and modifying predictors or models 252 based oncollected interaction data. The models 252 may include behavior modelsof customers or agents. The behavior models may be used to predictbehaviors of, for example, customers or agents, in a variety ofsituations, thereby allowing embodiments of the present invention totailor interactions based on the predictions or to allocate resources inpreparation for predicted characteristics of future interactions, andthereby improving overall performance, including improving the customerexperience. It will be appreciated that, while the analytics module 250is depicted as being part of a contact center, such behavior models maybe implemented on customer systems (or, as also used herein, on the“customer-side” of the interaction) and used for the benefit ofcustomers.

According to exemplary embodiments, the analytics module 250 may haveaccess to the data stored in the storage device 220, including thecustomer database 222 and agent database 223. The analytics module 250also may have access to the interaction database 224, which may storedata related to interactions and interaction content (e.g., transcriptsof the interactions and events detected therein), interaction metadata(e.g., customer identifier, agent identifier, medium of interaction,length of interaction, interaction start and end time, department,tagged categories), and the application setting (e.g., the interactionpath through the contact center). As discussed more below, the analyticmodule 250 may be further configured to retrieve data stored within thestorage device 220 for use in developing and training algorithms andmodels 252, for example, by applying machine learning techniques.

One or more of the models 252 may be configured to predict customer oragent behavior and/or aspects related to contact center operation andperformance. Further, one or more of the models 252 may be used innatural language processing and, for example, include intent recognitionand the like. The models 252 may be developed based upon 1) known firstprinciple equations describing a system, 2) data, resulting in anempirical model, or 3) a combination of known first principle equationsand data. In developing a model for use with present embodiments,because first principles equations are not available or easily derived,it is generally preferred to build an empirical model based uponcollected and stored data. To properly capture the relationship betweenthe manipulated/disturbance variables and the controlled variables ofcomplex systems, the models 252 preferably are nonlinear. This isbecause nonlinear models can represent curved rather than straight-linerelationships between manipulated/disturbance variables and controlledvariables, which are common to complex systems such as those discussedherein. Given the foregoing requirements, a neural network-basedapproach is presently a preferred embodiment for implementing the models252. Neural networks, for example, may be developed based upon empiricaldata using advanced regression algorithms.

The analytics module 250 may further include an optimizer 254. As willbe appreciated, an optimizer may be used to minimize a “cost function”subject to a set of constraints, where the cost function is amathematical representation of desired objectives or system operation.As stated, the models 252 preferably are a non-linear model.Accordingly, the optimizer 254 may be a nonlinear programming optimizer.However, it is contemplated that the present invention may beimplemented by using, individually or in combination, a variety ofdifferent types of optimization approaches. These optimizationapproaches include, but not limited to, linear programming, quadraticprogramming, mixed integer non-linear programming, stochasticprogramming, global non-linear programming, genetic algorithms, andparticle/swarm techniques.

According to exemplary embodiments, the models 252 and the optimizer 254may together be used as an optimization system 255. For example, theanalytics module 250 may utilize the optimization system 255 as part ofan optimization process by which so aspect of contact center performanceand operational is enhanced or optimized, for example, aspects relatedto the customer experience, the agent experience, routing, function ofautomated processes, etc.

The various components, modules, and/or servers of FIG. 2 (as well asthe other figures included herein) may each include one or moreprocessors executing computer program instructions and interacting withother system components for performing the various functionalitiesdescribed herein. The computer program instructions may be stored in amemory implemented using a standard memory device, such as, for example,a random-access memory (RAM), or stored in other non-transitory computerreadable media such as, for example, a CD-ROM, flash drive, etc.Although the functionality of each of the servers is described as beingprovided by the particular server, a person of skill in the art shouldrecognize that the functionality of various servers may be combined orintegrated into a single server, or the functionality of a particularserver may be distributed across one or more other servers withoutdeparting from the scope of the present invention. Further, the terms“interaction” and “communication” are used interchangeably, andgenerally refer to any real-time and non-real-time interaction that usesany communication channel including, without limitation, telephone calls(PSTN or VoIP calls), emails, vmails, video, chat, screen-sharing, textmessages, social media messages, WebRTC calls, etc. Access to andcontrol of the components of the contact system 200 may be affectedthrough user interfaces (UIs) which may be generated on the customerdevices 205 and/or the agent devices 230. As noted above, the contactcenter system 200 may operate as a hybrid system in which some or allcomponents are hosted remotely, such as in a cloud-based or cloudcomputing environment.

Chat Systems

Turning to FIGS. 3, 4 and 5, various aspects of chat features andsystems are discussed, as may be utilized in exemplary embodiments ofthe present invention. As will be seen, the present invention mayinclude or be enabled by a chat feature by which textual messages areexchanged between different parties, where those parties may includelive persons, such as customers and agents, as well as automatedprocesses, such as bots or chatbots. In general, a bot (also known as anInternet bot, is a software application that runs automated tasks orscripts over the Internet. Typically, bots perform tasks that are bothsimple and structurally repetitive, at a much higher rate than would bepossible for a human alone. A chatbot is a particular type of bot and,as used herein, is defined as a piece of software that conducts aconversation via auditory or textual methods. As will be appreciated,chatbots are often designed to convincingly simulate how a human wouldbehave as a conversational partner. Chatbots are typically used indialog systems for various practical purposes including customer serviceor information acquisition. Some chatbots use sophisticated naturallanguage processing systems, but many simpler ones scan for keywordswithin the input, then pull a reply with the most matching keywords, orthe most similar wording pattern, from a database. Chatbots can beclassified into usage categories such as conversational commerce(e-commerce via chat), analytics, communication, customer support,social, travel, etc.

The chat features and systems are presented generally with reference toexemplary embodiments of a chat server, chatbot, and chat interfaceillustrated, respectively, in FIGS. 3, 4, and 5. While these examplesare provided with respect to a chatbot implemented on the contact centerside, it should be understood that such a chatbot may be modified towardimplementation on the customer-side. Accordingly, as discussed morebelow, it should be appreciated that the exemplary chat systems of FIGS.3, 4, and 5 may be modified by one skilled in the art for analogous useon the customer-side of interactions with contact centers. It will beappreciated that, as provided herein, chatbots may be utilized by voicecommunications via converting text-to-speech and/or speech-to-text.

Referring specifically now to FIG. 3, a more detailed schematic blockdiagram is provided of the chat server 240 introduced in relation toFIG. 2. As stated above, FIG. 3 is provided for background purposes andas an exemplary module for implementing a chat feature. The chat server240 may be coupled to (e.g., in electronic communication with) acustomer device 205 operated by the customer over a data communicationsnetwork 210. The chat server 240 may be operated by a business orenterprise as part of a contact center system 200 (e.g., FIG. 2) forimplementing and orchestrating aspects of chat conversations with thecustomers of the business, including both automated chats and chats withhuman agents. In regard to automated chats, the chat server 240 may hostone or more chat automation modules or chatbots 260A-260C (collectivelyreferenced as 260), which are configured with computer programinstructions for engaging in automated chat conversations. Thus,generally, the chat server 240 implements chat functionality, includingthe exchange of text-based or chat communications between a customerdevice 205 and an agent device 230 as well as between a customer device205 and a chatbot 260. As will be discussed more below, the chat server240 may include a customer interface module 265 and an agent interfacemodule 266 for generating particular UIs at the customer device 205 andthe agent device 230, respectively, that are included within the chatfunctionality.

The chatbots 260 may operate, for example, as an executable program thatcan be launched according to demand for the particular chatbot.According to an embodiment, the chat server 240 may operate as anexecution engine or environment for the chatbots 260, analogous toloading VoiceXML files to a media server for interactive voice response(IVR) functionality. Loading and unloading may be controlled by the chatserver 240, analogous to how a VoiceXML script may be controlled in thecontext of an interactive voice response. The chat server 240 mayprovide a means for capturing and collecting customer data in a unifiedway, similar to customer data capturing in the context of IVR. Such datacan be stored, shared, and utilized in a subsequent conversation,whether with the same chatbot, a different chatbot, an agent chat, oreven a different media type. According to an embodiment, the chat server240 is configured to orchestrate the sharing of data among the variouschatbots 260 as interactions are transferred or transitioned over fromone chatbot to another or from one chatbot to a human agent. Accordingto an embodiment, the data captured during interaction with a particularchatbot may be transferred along with a request to invoke a secondchatbot or human agent.

In exemplary embodiments, the number of chatbots 260 may vary accordingto the design and function of the chat server 240 and is not limited tothe number illustrated in FIG. 3. For example, different chatbots may becreated to have different profiles. The profile of a particular chatbotmay be used to select a chatbot with expertise for helping a customer ona particular subject control, for example, how the chatbot communicateswith a particular customer. Engaging chatbots with profiles that arecatered to specific types of customers may allow more effectivecommunication with such customers. For example, one chatbot may bedesigned or specialized to engage in a first topic of communication(e.g., opening a new account with the business), while another chatbotmay be designed or specialized to engage in a second topic ofcommunication (e.g., technical support for a product or service providedby the business), that is different from the first topic ofcommunication. In another example, the chatbots may be configured toutilize different dialects or slang or may have different personalitytraits or characteristics. For example, the vocabulary of the differentchatbots may be tailored to use the slang or diction of young people,elder people, people in a certain region of the country, and/or peoplehaving a certain language or ethnic background. The chat server 240 mayalso host a default chatbot that may be invoked at a beginning of a chatconversation if there is insufficient information about the customer toinvoke a more specialized chatbot. For example, if a customer intent isunknown when the conversation initially ensues, the default chatbot maybe invoked to ask questions about the customer intent. According to anembodiment, a chatbot may be customer selectable, for example, based onaccent, appearance, age group, language, etc., by way of a userinterface. Additionally, a chatbot may be assigned to a customer basedon demographic information of the customer. According to an embodiment,a chatbot profile may be selected based on information learned frompublicly available information (e.g., social media information) about acustomer.

According to an embodiment, a profile of a chatbot 260 may be stored ina profile database hosted in the storage device 220. The chatbot'sprofile data may include, without limitation, the chatbot's personality,gender, demographics, areas of expertise, and the like. According to anembodiment, for a given subject, including receptionist and conciergeservices, and specialists on particular products or services (e.g.,travel booking, opening accounts, etc.), there may be several differentchatbots 260, each with their own personality or profile.

Each of the different chatbots 260 may be configured, in conjunctionwith the chat server 240, to learn and evolve their behavior andresponses according to input by the customers. For example, in responseto customers reacting negatively to certain words, phrases, orresponses, the chatbots 260 may learn to use different words, phrases,or responses. Such learning may be supervised in order to preventundesired evolution of the personalities or profiles of the chatbots260. For example, changes to the personalities or profiles of thechatbots 260 may be first approved or validated by human supervisors,certain keywords or phrases may be identified or flagged, and customerfeedback may be analyzed. According to an embodiment, different chatbots260 may be configured to learn from each other, in addition to learningbased on customer feedback or agent feedback. For example, differentchatbots 260 may be configured to communicate and exchange data witheach other. In exemplary embodiments, the different chatbots 260 mayoperate as a neural network for deep learning and self-learningcapabilities, by exchanging data with one another.

As mentioned, the chat server 240 may include a customer interfacemodule 265 and an agent interface module 266. The customer interfacemodule 265 may be configured to generating user interfaces (UIs) fordisplay on the customer device 205 that facilitate chat communicationbetween the customer and the chatbots 260 and the customer and humanagents. The chat server 240 may include an agent interface module 266for generating particular UIs on the agent device 230 that facilitatechat communication between an agent operating an agent device 230 and acustomer operating a customer device 205. The agent interface module 266may also generate UIs on the agent device 230 that allow an agent tomonitor aspects of an ongoing chat between a chatbot 260 and a customer.The customer interface module 265 and the agent interface module 266,thus, may operate to facilitate the exchange of chat communicationsbetween the customer device 205 and one of the chatbots 260 and/or oneof the agent devices 230. For example, the customer interface module 265may transmit signals to the customer device 205 during a chat sessionthat are configured to generated particular UIs on the customer device205. As will be seen, those UIs generated on the customer device 205 mayinclude the text messages sent from chatbots 260 or human agents as wellas other non-text graphics that are intended to accompany the textmessages, such as, emoticons or animations, for display therewith.Likewise, the agent interface module 266 may transmit signals to theagent device 230 during a chat session that are configured to generatedparticular UIs on the agent device 230. As will be seen, those UIsgenerated on the agent device 230 may include the text messages sentfrom customer device 205. The UIs generated on the agent device 230 alsomay include an interface that facilitates the selection of non-textgraphics by the agent that are to accompany an outgoing text message tothe customer.

According to an embodiment, the chat server 240 may be implemented in alayered architecture, with a media layer, a media control layer, and thechatbots executed by way of the IMR server 216 (similar to executing aVoiceXML on an IVR media server).

As depicted in FIG. 2, the chat server 240 may further be configured tointeract with the knowledge management server 234 to query the serverfor knowledge information. The query, for example, may be based on aquestion received from the customer during a chat. Responses receivedfrom the knowledge management server 234 may then be provided to thecustomer as part of a chat response.

According to an embodiment, the chat server 240 may run on the samecomputer as the other servers of the contact center system 200 depictedin FIG. 2. The chat server 240 may also run on a separate computerequipped with a processor, which executes program instructions andinteracts with other system components to perform various methods andoperations according to embodiments of the present invention. The chatserver 240 may also run on the cloud or serverless architecture. Thechat server 240 may include a memory, which operates as an addressablememory unit for storing software instructions to be executed by theprocessor. The memory may be implemented using any suitable memorydevice, such as a random access memory (RAM), and may additionallyoperate as a computer readable storage medium having non-transitorycomputer readable instructions stored therein that, when executed by theprocessor, cause the processor to control and manage an automated chatcommunication between the chat server 240, the customer device 205,and/or the agent device 230.

Referring specifically now to FIG. 4, a more detailed block diagram isprovided of an exemplary chat automation module or chatbot 260. Asstated, FIG. 4 is provided for background purposes and as an exemplaryimplementation of a chatbot. As would be understood by one of ordinaryskill in the art, aspects of chatbot 260 may be used or modified for usewith embodiments of the present invention. In the illustratedembodiment, the chatbot 260 includes a text analytics module 270, adialog manager 272, and an output generator 274. The text analyticsmodule is configured to analyze and understand natural language. In thisregard, the text analytics module may be configured with a lexicon ofthe language, a syntactic/semantic parser, and grammar rules forbreaking a phrase provided by the customer device 205, into an internalsyntactic and semantic representation. According to an embodiment, theconfiguration of the text analytics module depends on the particularprofile associated with the chatbot. For example, certain slang wordsmay be included in the lexicon for one chatbot but excluded from anotherchatbot.

In operation, the dialog manager 272 receives the syntactic and semanticrepresentation from the text analytics module 270 and manages thegeneral flow of the conversation based on a set of decision rules. Inthis regard, the dialog manager 272 maintains history and state of theconversation, and generates an outbound communication based on thehistory and state. The communication may follow the script of aparticular conversation path selected by the dialog manager 272. Asdescribed in further detail below, the conversation path may be selectedbased on an understanding of a particular purpose or topic of theconversation. The script for the conversation path may be generatedusing any of various languages and frameworks conventional in the art,such as, for example, artificial intelligence markup language (AIML),SCXML, or the like.

During the chat conversation, the dialog manager 272 selects a responsedeemed to be appropriate at the particular point of the conversationflow/script, and outputs the response to the output generator 274.According to an embodiment, the dialog manager 272 may also beconfigured to compute a confidence level for the selected response andprovide the confidence level to the agent device 230. According to anembodiment, every segment, step, or input in a chat communication mayhave a corresponding list of possible responses. Responses may becategorized based on topics (determined using a suitable text analyticsand topic detection scheme) and suggested next actions are assigned.Actions may include, for example, responses with answers, additionalquestions, assignment for a human agent to assist (e.g., bydisambiguating input from the customer), and the like. The confidencelevel may be utilized to assist the system with deciding whether thedetection, analysis, and response to the customer input is appropriateor sufficient, or whether a human agent should be involved. For example,a threshold confidence level may be assigned to invoke human agentintervention, based on one or more business rules. According to anembodiment, confidence level may be determined based on customerfeedback. For example, in response to detecting a negative reaction froma customer to an action or response taken by the chatbot, the confidencelevel may be reduced. Conversely, in response to detecting a positivereaction from a customer, the confidence level may be increased.

According to an embodiment, the response selected by the dialog manager272 may include information provided by the knowledge management server234. The information may be, for example, a link to a knowledge articlethat the chatbot may want to recommend to the customer in response to aquestion posed by the customer.

In exemplary embodiments, the output generator 274 takes the semanticrepresentation of the response provided by the dialog manager 272, mapsthe response to a chatbot profile or personality (e.g., by adjusting thelanguage of the response according to the dialect, vocabulary, orpersonality of the chatbot), and outputs an outbound text to bedisplayed at the customer device 205. The output text may beintentionally presented such that the customer interacting with achatbot is unaware that it is interacting with an automated process asopposed to a human agent. As will be seen, in accordance with otherembodiments, the output text may be linked with visual representations,such as emoticons or animations, integrated into the customer's userinterface.

Brief reference will now be made to FIG. 5, in which a webpage 280having an exemplary implementation of a chat feature 282 is presented.The webpage 280, for example, may be associated with a business orenterprise website and intended to initiate interaction betweenprospective or current customers visiting the webpage and a contactcenter associated with the enterprise. As will be appreciated, the chatfeature 282 may be generated on any type of customer device 205,including personal computing devices such as laptops, tablet devices, orsmart phones, to name a few non-limiting examples. Further, the chatfeature 282 may be generated as a window within a webpage or implementedas a full-screen interface. As in the example shown, the chat feature282 may be contained within a defined portion of the webpage 280 and,for example, may be implemented as a widget via the systems andcomponents described above and/or any other conventional means. As willbe appreciated, the chat feature 282 may include an exemplary way forcustomers to enter text messages for delivery to a contact centerassociated with a particular organization or enterprise.

As an example, the webpage 280 may be accessed by a customer via acustomer device, which provides a communication channel for interactingor chatting with bots or live agents. In exemplary embodiments, asshown, the chat feature 282 includes an interface generated on a screenof the customer device, such as customer device 205. This user interfaceof the chat feature 282 may be referred to herein as a customer chatinterface 284. The customer chat interface 284, for example, may begenerated by a customer interface module of a chat server, as alreadydescribed. The customer interface module may send signals to thecustomer device that are configured to generate a desired customer chatinterface 284 in accordance with the content of a chat message issued bya chat source, which as depicted is a chatbot named “Kate”. The customerchat interface 284 may be contained within a designated area or window,with that window covering a designated portion of the webpage 280. Thecustomer chat interface 284 also may be a text display area 286, whichis the area dedicated to the display of received and sent text messages.and a text input area 288, which facilitates the customer's input oftext messages. Though this may be achieved in other ways, the chatinterface of FIG. 5 illustrates one manner by which text messages may beentered by customers for communicating with an agent or chatbot of acontact center.

Before proceeding further with the description of the present invention,an explanatory note will be provided in regard to referencing systemcomponents—e.g., modules, servers, and other components—that havealready been introduced in the previous figures. Whether or not asubsequent reference includes the corresponding numerical identifiers ofFIGS. 1-5, it should be understood that the reference incorporates thepreviously discussed examples and, unless otherwise specificallylimited, may be implemented in accordance with those examples and/orusing other conventional technology capable of fulfilling the desiredfunctionality, as would be understood by one of ordinary skill in theart. Thus, for example, subsequent mention of a “contact center” shouldbe understood as referring to the exemplary “contact center system 200”of FIG. 2 and/or other conventional technology for implementing acontact center. As additional examples, a subsequent mention below to a“customer device”, “agent device”, “chat server”, “computing device”,“chatbot”, or “customer interface module” should be understood asreferring to the exemplary “customer device 205”, “agent device 230”,“chat server 240”, “computing device 200”, “chatbot 260”, or “customerinterface module 265”, respectively, of FIGS. 1-5, as well asconventional technology for fulfilling the same functionality.

Personalized Customer Automation Systems

Turning now to FIGS. 6 through 14, embodiments of the present inventioninclude systems and methods for automating and augmenting customeractions during various stages of interaction with a customer serviceprovider or contact center, with those various interaction stages beingclassified as pre-contact, during-contact, and post-contact stages (orpre-interaction, during-interaction, and post-interaction stages).Additionally, embodiments of the present invention include systems andmethods related to personalized customer profiles and automated routing.

With specifically reference now to FIG. 6, an exemplary customerautomation system 300 is shown which may be used in methods and systemsof the present invention. To better explain how the customer automationsystem 300 functions, reference will also be made to FIG. 7, which showsa flowchart 350 of an exemplary method for automating customer actionswhen interacting with contact centers. Additional information related tocustomer automation and related systems and methods are provided in U.S.application Ser. No. 16/151,362, filed on Oct. 4, 2018, entitled “Systemand Method for Customer Experience Automation”, the content of which isincorporated herein by reference. As will be seen, the customerautomation system 300 may be used as part of an automated personalassistant (or “personal bot”) 405, which is introduced in the discussionrelated to FIG. 8.

The customer automation system 300 of FIG. 6 represents a system thatmay be generally used for customer-side automations, which, as usedherein, refers to the automation of actions on behalf of a customer ininteractions with customer service providers or contact centers [.] Suchinteractions may also be referred to as “customer-contact centerinteractions” or simply “customer interactions”. Further, in discussingsuch customer-contact center interactions, it should be appreciated thatreference to a “contact center” or “customer service provider” isintended to generally refer to any customer service department or otherservice provider associated with an organization or enterprise (such as,for example, a business, governmental agency, non-profit, school, etc.)with which a user or customer has business, transactions, affairs orother interests.

In exemplary embodiments, the customer automation system 300 may beimplemented as a software program or application running on a mobiledevice or other computing device, cloud computing devices (e.g.,computer servers connected to the customer device 205 over a network),or combinations thereof (e.g., some modules of the system areimplemented in the local application while other modules are implementedin the cloud. For the sake of convenience, embodiments of the presentinvention may be primarily described in the context of implementationvia an application running on the customer device 205. However, itshould be understood that present embodiments are not limited thereto.

The customer automation system 300 may include several components ormodules. For example, as shown, the customer automation system 300 mayinclude a user interface 305, a natural language processing (NLP) module310, an intent inference module 315, a script storage module 320, ascript processing module 325, a customer profile database or module (orsimply “customer profile”) 330, a communication manager module 335, atext-to-speech module 340, a speech-to-text module 342, and anapplication programming interface (API) 345, each of which will bedescribed with more particularity with reference also to flowchart 350of FIG. 7. It will be appreciated that some of the components of andfunctionalities associated with the customer automations system 300 mayoverlap with the chatbot systems described above in relation to FIGS. 3,4, and 5. In cases where the customer automation system 300 and suchchatbot systems are employed together as part of a customer-sideimplementation—such as in the example of the personal bot 405 of FIG.8—it is anticipated that such overlap may include the sharing ofresources between the two systems.

In an example of operation, with specific reference now to the flowchart350 of FIG. 7, the customer automation system 300 receives input at aninitial step or operation 355. Such input may come from several sources.For example, a primary source of input may be the customer, where suchinput is received via the user interface 305 on the customer device(e.g., customer device 205). The input also may include data receivedfrom other parties, particularly parties interacting with the customerthrough the customer device. For example, information or communicationssent to the customer from the contact center may provide aspects of theinput. In either case, the input may be provided in the form of freespeech or text (e.g., unstructured, natural language input). Input alsomay include other forms of data received or stored on the customerdevice.

Continuing with the flow diagram 350, at an operation 360, the customerautomation system 300 parses the natural language of the input using theNLP module 310 and, therefrom, infers a customer intent using the intentinference module 315. That is, the customer's intent is determined giventhe input. For example, where the customer input is provided as speech,the speech may be transcribed into text by a speech-to-text system (suchas a large vocabulary continuous speech recognition or LVCSR system) aspart of the parsing by the NLP module 310. The transcription may beperformed locally on the customer device 205 or the speech may betransmitted over a network for conversion to text by a cloud-basedserver. In certain embodiments, for example, the intent inference module315 may automatically infer the customer's intent from the text of thecustomer input using artificial intelligence or machine learningtechniques. These artificial intelligence techniques may include, forexample, identifying one or more keywords from the customer input andsearching a database of potential intents corresponding to the givenkeywords. The database of potential intents and the keywordscorresponding to the intents may be automatically mined from acollection of historical interaction recordings. In cases where thecustomer automation system 300 fails to understand or completelyunderstand the intent from the customer's input, a selection of severalintents may be provided to the customer in the user interface 305. Thecustomer may then clarify his/her intent by selecting one of thealternatives or may request that other alternatives be provided.

After the customer's intent is determined, the flowchart 350 proceeds toan operation 365 where the customer automation system 300 loads a scriptassociated with the given intent. Such scripts, for example, may bestored and retrieved from the script storage module 320. As will beappreciated, the script may include a set of commands or operations,pre-written speech or text, and/or fields of parameters or data (also“data fields”), which represent data that is expected to be required inautomating an action for the customer. For example, the script mayinclude commands, text, and data fields that will be required tocomplete an interaction with a contact center in order to resolve theissue specified by the customer's intent. Scripts may be specific to aparticular contact center (or a particular organization) and, inexemplary embodiments, may be further tailored to resolving a particularissue. Scripts may be organized in a number of ways. In exemplaryembodiments, the scripts are organized in a hierarchical fashion, suchas where all scripts pertaining to a particular organization are derivedfrom a common “parent” script that defines common features. An exampleof common features might be common templates for authentication steps(e.g., account numbers and verification codes), where “child” scriptsinclude templates for the different types of issues to be resolved(e.g., double billing, requests for reductions in price, servicepausing, service plan modification, service cancellation, and the like).In exemplary embodiments, rather than a hierarchical relationship, thescripts are assembled from common tasks, such as combining“authentication” templates for authenticating with various serviceproviders and “issue” templates for resolving common issues that may beassociated with multiple providers.

The scripts may be produced via mining data, actions, and dialogue fromprevious customer interactions. Specifically, the sequences ofstatements made during a request for resolution of a particular issuemay be automatically mined from a collection of historical interactionsbetween customers and customer service providers. Systems and methodsmay be employed for automatically mining effective sequences ofstatements and comments, as described from the contact center agentside, are described in U.S. patent application Ser. No. 14/153,049“Computing Suggested Actions in Caller Agent Phone Calls By UsingReal-Time Speech Analytics and Real-Time Desktop Analytics,” filed inthe United States Patent and Trademark Office on Jan. 12, 2014, theentire disclosure of which is incorporated by reference herein.

With the script retrieved, the flowchart 350 proceeds to an operation370 where the customer automation system 300 processes or “loads” thescript. This action may be performed by the script processing module325, which performs it by filling in the data fields of the script withappropriate data pertaining to the customer. More specifically, thescript processing module 325 may extract customer data that is relevantto the anticipated interaction, with that relevance being predeterminedby the script selected as corresponding to the customer's intent.According to preferred embodiments, the data for some or most of thedata fields within the script may be automatically loaded with dataretrieved from customer data stored within the customer profile 330. Aswill be appreciated, the customer profile 330 may store particular datarelated to the customer, for example, the customer's name, birth date,address, account numbers, authentication information, and other types ofinformation relevant to customer service interactions. The data selectedfor storage within the customer profile 330 may be based on data thecustomer has used in previous interactions and/or include data valuesobtained directly by the customer. In case of any ambiguity regardingthe data fields or missing information within a script, the scriptprocessing module 325 may include functionality that prompts and allowsthe customer to manually input the needed information.

Referring again to the flowchart 350, at an operation 375, the loadedscript may be transmitted to the customer service provider or contactcenter. As discussed more below, the loaded script may include commandsand customer data necessary to automate at least a part of aninteraction with the contact center on the customer's behalf. Inexemplary embodiments, the API 345 is used so to interact with thecontact center directly. Contact center may define a protocol for makingcommonplace requests to their systems, which is provided for in the API345. Such APIs may be implemented over a variety of standard protocolssuch as Simple Object Access Protocol (SOAP) using Extensible MarkupLanguage (XML), a Representational State Transfer (REST) API withmessages formatted using XML or JavaScript Object Notation (JSON), andthe like. Accordingly, the customer automation system 300 mayautomatically generate a formatted message in accordance with a definedprotocol for communication with a contact center, where the messagecontains the information specified by the script in appropriate portionsof the formatted message.

Personal Bot

With reference now to FIG. 8, an exemplary system 400 is shown thatincludes an automated personal assistant or, as referred to herein,personal bot 405. As will be seen, the personal bot 405 is configured toautomate aspects of interactions with a customer service provider onbehalf of a customer. As stated above, present invention relates tosystems and methods for automating aspects of the customer-side of theinteractions between customers and customer service providers or contactcenters. Accordingly, the personal bot 405 may provide ways to automateactions that customers are required to perform when contacting,interacting, or following up with contact centers.

The personal bot 405, as used herein, may generally reference anycustomer-side implementation of any of the automated processes orautomation functionality described herein. Thus, it should be understoodthat, unless otherwise specifically limited, the personal bot 405 maygenerally employ any of the technologies discussed herein—includingthose related to the chatbots 260 and the customer automation system300—to enable or enhance automation services available to customers. Forexample, as indicated in FIG. 8, the personal bot 405 may include thefunctionality of the above-described customer automation system 300.Additionally, the personal bot 405 may include a customer-sideimplementation of a chatbot (for example, the chatbot 260 of FIGS. 4 and5), which will be referred herein as a customer chatbot 410. As will beseen, the customer chatbot 410 may be configured to interact privatelywith the customer in order to obtain feedback and direction from thecustomer pertaining to actions related to ongoing, future, or pastinteractions with contact centers. Further, the customer chatbot 410 maybe configured to interact with live agents or chatbots associated with acontact center on behalf of the customer.

As shown in FIG. 8, in regard to system architecture, the personal bot405 may be implemented as a software program or application running on amobile device or personal computing device (shown as a customer device205) of the customer. For example, the personal bot 405A may includelocally stored modules, including the customer automation system 300,the customer chatbot 410, and elements of the customer profile 330A. Thepersonal bot 405 also may include remote or cloud computing components(e.g., one or more computer servers or modules connected to the customerdevice 205 over a network 210), which may be hosted in a cloud computingenvironment or cloud 415 (see cloud hosted elements of the personal bot405B). For example, as shown in the illustrated example, the scriptstorage module 320 and elements of the customer profile 330B may bestored in databases in the cloud 415. It should be understood, however,that present embodiments are not limited to this arrangement and, forexample, may include other components being implemented in the cloud415.

Accordingly, as will be seen, embodiments of the present inventioninclude systems and methods for automatically initiating and conductingan interaction with a contact center to resolve an issue on behalf of acustomer. Toward this objective, the personal bot 405 may be configuredto automate particular aspects of interactions with a contact center onbehalf of the customer. Several examples of these types of embodimentswill now be discussed in which resources described herein—including thecustomer automation system 300 and customer chatbot 410—are used toprovide the necessary automation. In presenting these embodiments,reference is again made to previously incorporated U.S. application Ser.No. 16/151,362, entitled “System and Method for Customer ExperienceAutomation”, which includes further background and other supportingmaterials.

Pre-Interaction Automation

Embodiments of the present invention include the personal bot 405 andrelated resources automating one or more actions or processes by whichthe customer initiates a communication with a contact center forinteracting therewith. As will be seen, this type of automation isprimarily aimed at those actions normally occurring within thepre-contact or pre-interaction stage of customer interactions.

For example, in accordance with an exemplary embodiment, when a customerchooses to contact a contact center, the customer automation system 300may automate the process of connecting the customer with the contactcenter. For example, present embodiments may automatically navigate anIVR system of a contact center on behalf of the customer using a loadedscript. Further, the customer automation system 300 may automaticallynavigate an IVR menu system for a customer, including, for example,authenticating the customer by providing authentication information(e.g., entering a customer number through dual-tone multi-frequency orDTMF or “touch tone” signaling or through text to speech synthesis) andselecting menu options (e.g., using DTMF signaling or through text tospeech synthesis) to reach the proper department associated with theinferred intent from the customer's input. More specifically, thecustomer profile 330 may include authentication information that wouldtypically be requested of customers accessing customer support systemssuch as usernames, account identifying information, personalidentification information (e.g., a social security number), and/oranswers to security questions. As additional examples, the customerautomation system 300 may have access to text messages and/or emailmessages sent to the customer's account on the customer device 205 inorder to access one-time passwords sent to the customer, and/or may haveaccess to a one-time password (OTP) generator stored locally on thecustomer device 205. Accordingly, embodiments of the present inventionmay be capable of automatically authenticating the customer with thecontact center prior to an interaction.

In addition, the customer automation system 300 may facilitate acustomer contacting a contact center via multiple channels forcommunication, such as, call (e.g., voice and/or video), chat, ore-mail. In exemplary embodiments, the communication channels may includecalling, chatting, and leaving a message. Estimated wait times forinteractions with a live agent (e.g., call or chat) may also be shown tothe customer. For example, if the customer chooses to call and speakwith a live agent, the customer may be offered several options. Theseoptions might include to wait (e.g., “dial now and wait”), select acallback (e.g., “dial now and skip waiting”), or schedule a call for agiven time (e.g., “schedule a callback”). In exemplary embodiments, ifthe customer selects to schedule a call for a given time by opting for“schedule a callback,” for example, the customer automation system 300may access the customer's calendar (stored/accessible on the samecustomer device 205) and offer suggestions for free times in thecustomer's calendar. The customer automation system 300 may determinethat the customer is free at particular times over the next two days.These times may be automatically presented to the customer for selectionthereby. The customer may also choose to schedule the call at anothertime and input this into the user interface 305. Certain embodiments ofthe present invention may enable callback scheduling even when contactcenters do not directly support such a feature. For example, assumingthat the customer has scheduled a callback for 10:00 am, the system mayautomatically determine the approximate wait time during the timeperiods leading up to 10:00 am. This might be based on historical datacaptured from other customers contacting this particular organization orit may be based on wait time data published by the contact center. Thus,in accordance with exemplary embodiments, the customer automation system300 automatically connects to the contact center at a time prior to thescheduled call back time, based on the expected wait time, and suppliesthe set of information provided to the customer automation system 300 inaccordance with the script in order to be placed on hold by the contactcenter. For example, the customer automation system 300 mayautomatically determine that the expected wait time at 09:15 is 45minutes, and therefore initiates communication with the contact centerat 09:15 in order have an agent available to speak to the customer ataround 10:00. When the customer automation system 300 is connected to alive contact center agent (e.g., by detecting a ringing on the contactcenter end of the communication channel or by detecting a voice saying“hello”), an automatic notification may be sent to the customer (e.g.,by ringing at the customer device 205) and then the customer may beconnected to the live agent.

In accordance with other embodiments, the customer automation system 300may automate a process for preparing an agent before a call from acustomer. For example, the customer automation system 300 may send arequest that the agent study certain materials provided by the customerbefore the live call happens.

During-Interaction Automation

Embodiments of the present invention further include the personal bot405 and related resources automating the actual interaction (or aspectsthereof) between the customer and a contact center. As will be seen,this type of automation is primarily aimed at those actions normallyoccurring within the during-contact or during-interaction stage ofcustomer interactions.

For example, the customer automation system 300 may interact withentities within a contact center on behalf of the customer. Withoutlimitation, such entities may include automated processes, such aschatbots, and live agents. Once connected to the contact center, thecustomer automation system 300 may retrieve a script from the scriptstorage module 320 that includes an interaction script (e.g., a dialoguetree). The interaction script may generally consist of a template ofstatements for the customer automation system 300 to make to an entitywithin the contact center, for example, a live agent. In exemplaryembodiments, the customer chatbot 410 may interact with the live agenton the customer's behalf in accordance with the interaction script. Asalready described, the interaction script (or simply “script”) mayconsist of a template having defined dialogue (i.e., predetermined textor statements) and data fields. As previously described, to “load” thescript, information or data pertinent to the customer is determined andloaded into the appropriate data fields. Such pertinent data may beretrieved from the customer profile 330 and/or derived from inputprovided by the customer through the customer interface 305. Accordingto certain embodiments, the customer chatbot 410 also may be used tointeract with the customer to prompt such input so that all of thenecessary data fields within the script are filled. In otherembodiments, the script processing module 325 may prompt the customer tosupply any missing information (e.g., information that is not availablefrom the customer profile 330) to fill in blanks in the template throughthe user interface 305 prior to initiating a communication with thecontact center. In certain embodiments, the script processing module 325may also request that the customer confirm the accuracy of all of theinformation that the customer automation system 300 will provide to thecontact center.

Once the loaded script is complete, for example, the interaction withthe live agent may begin with an initial statement explaining the reasonfor the call (e.g., “I am calling on behalf of your customer, Mr. ThomasAnderson, regarding what appears to be double billing.”), descriptionsof particular details related to the issue (e.g., “In the previous threemonths, his bill was approximately fifty dollars. However, his mostrecent bill was for one hundred dollars.”), and the like. While suchstatements may be provided in text to the contact center, it may also beprovided in voice, for example, when interacting with a live agent. Inregard to how such an embodiment may function, a speech synthesizer ortext-to-speech module 340 may be used to generate speech to betransmitted to the contact center agent over a voice communicationchannel. Further, speech received from the agent of the contact centermay be converted to text by a speech-to-text converter 342, and theresulting text then may be processed by the customer automation system300 or customer chatbot 410 so that an appropriate response in thedialogue tree may be found. If the agent's response cannot be processedby the dialogue tree, the customer automation system 300 may ask theagent to rephrase the response or may connect the customer to the agentin order to complete the transaction.

While the customer automation system 300 is conducting the interactionwith the live agent in accordance with the interaction script, the agentmay indicate his or her readiness or desire to speak to the customer.For the agent, readiness might occur after reviewing all of the mediadocuments provided to the agent by the customer automation system 300and/or after reviewing the customer's records. In exemplary embodiments,the script processing module 325 may detect a phrase spoken by the agentto trigger the connection of the customer to the agent via thecommunication channel (e.g., by ringing the customer device 205 of thecustomer). Such triggering phrases may be converted to text by thespeech-to-text converter 342 and the natural language processing module310 then may determine the meaning of the converted text (e.g.,identifying keywords and/or matching the phrase to a particular clusterof phrases corresponding to a particular concept).

As another example, the customer automation system 300 may presentautomatically generated “quick actions” to the customer based on thecustomer's inferred intent and other data associated with the ongoinginteraction. In some circumstances, the “quick actions” require nofurther input from the customer. For example, the customer automationsystem 300 may suggest sending an automatically generated text or emailmessage to the contact center directly from a main menu screen, wherethe message describes the customer's issue. The message may be generatedautomatically by the script processing module based on a messagetemplate provided by the script, where portions of the template thatcontain customer-specific and incident-specific data are automaticallyfilled in based on data collected about the customer (e.g., from thecustomer profile) and that the customer has supplied (e.g., as part ofthe initial customer input). For example, in the case where the customerinput references a question about a possible double billing by aparticular service provider, the script processing module 325 canreference previous billing statements, which may be stored as part ofthe customer profile 330, to look for historical charges. The customerautomation system 300 infers from these previous billing statements thatthe amount charged for the period in question was unusually high. Insuch cases, the system may automatically generate a message which maycontain the information about the customer's typical bills and theproblem with the current bill. The customer can direct the customerautomation system 300 to send the automatically generated messagedirectly to the contact center associated with the service provider. Inexemplary embodiments, the script may provide multiple templates, andthe customer may select from among the templates and/or edit a messageprior to sending, in order to match the customer's personality orpreferred tone of voice.

In other exemplary embodiments, the personal bot 405 may automateprocesses that augment a current or ongoing interaction between thecustomer and a contact center (e.g., between the customer and either achatbot or a live agent of the contact center). While the personal bot405 may not handle the interaction in such embodiments, the personal botmay work behind the scenes to facilitate the customer's interaction witha contact center, so to increase the likelihood of a desirable outcomefor the customer. In such embodiments, once the interaction has beeninitiated with a live agent, meta-data regarding the interaction may bedisplayed to the customer in the user interface 305. This may be donethroughout the interaction, with the information being update based onthe progression of the ongoing interaction. Examples of such informationmight include, but not be limited to, name of the contact center, nameof the department reached, reason for the call, name of the contactcenter agent, name of other agents who were on the call, etc. Accordingto exemplary embodiments, this type of information may include atranscript of the ongoing call so that the customer can easily look backat previous statements. In addition, the customer automation system 300may display other types of information to the customer that is foundpertinent given, for example, the recognition of certain key wordswithin the transcript of the ongoing conversation. That is, the customerautomation system 300 may push relevant content from a knowledge base(e.g., the knowledge system 238 of FIG. 2) to the customer given thecontent of the transcript of the interaction.

The customer automation system 300 also may enable the customer andagent to share relevant content with each other throughout theinteraction. For example, in one embodiment, the agent or customer mayshare screens, documents (contracts, warranties, etc.), photos, andother files with each other. Other files may also be shared, such asscreenshots of content captured by one of the parties during theconversation, a current view from a camera, links, photographs of brokenor failed products, screenshots of error messages, copies of documents,proofs of purchase, or any other supporting file. The customerautomation system 300, thus, may provide functionality that facilitatesthe customer supplying or sharing additional or augmenting material toan agent of the contact center that is relevant to an ongoinginteraction. To do this, for example, a supplemental communicationchannels (e.g., a data channel) is established in parallel to theprimary communication channel (e.g., a voice communication channel or atext communication channel) to transfer the augmenting informationbetween the customer and the contact center agent. In certainembodiments, these documents may be provided along with an automaticallygenerated “quick actions” message. For example, such quick actionmessages may prompt the customer to take a photo of the broken part, forinclusion in the shared material.

In accordance with other embodiments, the communication manager 335monitors conditions for a customer based on specified intents andautomatically generates notifications to be presented to the customerthrough the user interface 305. For example, based the previous activityof the customer (for example, the customer's billing statements, whichmay be stored in the customer profile 330, and communications fromdifferent contact centers), the communication manager 335 mayautomatically generate notifications which might be of interest to thecustomer. Examples of a notification generated by the communicationmanager may include a reminder about the upcoming expiration of a deal,an offer of a new deal, actions for the customer, and the like. Forexample, the notification may offer quick actions that can be performeddirectly from the notification screen, such as how to get a specificdeal, call a contact center about a specific deal, search for moredeals, cancel a service, etc. The communication manager 335 maycustomize notifications to the customer based on the customer's previousdeals, billing statements, crowd-sourced information about how similarcustomers reacted to deals, personal preferences, and the like. Thecommunication manager 335 may provide such functionality through theuser interface 305 for the customer to search for more deals based ontheir needs. Should the customer select this option, the customerautomation system 300 may present some relevant deals that areidentified from a database of deals.

In accordance with other embodiments, the customer automation system 300may provide ‘end of deal’ notifications. In such cases, the customer isinformed about the ending of deal, contract, business arrangement, orthe like. For example, a customer may be notified about the ending of aninternet package deal with their current internet service provider(ISP). The customer may be presented with the best deals offered bytheir current ISP and the best deals offered by other ISPs. Continuingwith this example, the customer automation system 300 may offer specificdeals without requiring communication with the contact center, such as acall-in to the relevant customer service department. Pricing may also beshown along with other comparisons relevant to the customer. Forexample, promotional offers may be compared to the average usage of thecustomer (e.g., based on the customer profile) and current pricing oftheir plan. Other suggested options that are specific to the customerintent in the notification may also be presented, such as a “cancelservice” option and an option to “search more deals.” Should thecustomer select the “cancel service” option, the customer automationsystem 300 may send a cancellation request to the contact centerautomatically. The customer automation system 300 may also search formore deals which fit the customer's needs and present these whether thecustomer has selected to cancel their service or just search foradditional deals. These may also be presented to the customer.

According to other embodiments, the customer automation system 300 maymonitor statements made by the contact center agent and automaticallyoffers guidance to the customer in real-time. For example, the customerautomation system 300 converts the contact center agent's speech to textusing the speech-to-text converter 342 and processes the text using thenatural language processing module 310. In exemplary embodiments, thenatural language processing module 310 detects when the agent is makingan offer and compares it to a database of other offers made by agents ofthe organization that the customer is speaking with. This database ofother offers is crowdsourced from other customers. After identifying acorresponding matching offer in the database, the ranking of the offercompared to other offers is identified in order to determine whether theagent could make a better offer.

According to still other embodiments, the customer automation system 300may present information to the customer about prior interactions with aparticular contact center or organization. For example, such informationmay be retrieved during an ongoing interaction to show the current agentwhat other agents have said.

Customer Privacy Automation

Embodiments of the present invention further include the personal bot405 and related resources functioning to automate aspects related toprivacy for a customer. More particularly, the customer automationsystem 300 of the personal bot 405 may allow customers to manage privacyor data sharing with organizations and corresponding contact centers.

In accordance with exemplary embodiments, for example, the customerautomation system 300 may facilitate the customer managing settings forprivacy and data sharing (or simply “data sharing settings”) globally,for example, across all providers and data types. The customer isenabled to manage data sharing settings on a per-organization basis bychoosing which data type to share with each specific organization. Asanother example, the customer is enabled to manage data (e.g., datawithin a customer profile) according to data type. In such cases, thecustomer may choose which organization or which types of organizationsto share each particular data type. In more detail, each field of datain the customer profile may be associated with at least one permissionsetting (e.g., in exemplary embodiments, each field of data may have adifferent permission setting for each provider). Further, userinterfaces may be provided through the customer device 205 that allowthe customer to adjusting data sharing settings and/or permissionsettings. Within such user interfaces, data sharing settings orpermission settings may be made adjustable on a per data type, perorganization basis, per type of organization basis, etc.

In accordance with exemplary embodiments, the customer automation system300 may offer a plurality of levels for data sharing settings orpermission settings. For example, in one embodiment, three differentlevels of permission settings are offered: share data, share anonymousdata, and do not share any data. Anonymous data may include, forexample, genericized information about the customer such as gender, zipcode of residence, salary band, etc. Some aspects of embodiments of thepresent invention may enable compliance with the General Data ProtectionRegulation (GDPR) of the European Union (EU). In other embodiments, thecustomer automation system 300 provides functionality for a customer toexercise the “right to be forgotten” with all organizations (e.g.,providers and/or business) that the customer has interacted with. Inother embodiments, the customer can switch on/off the sharing of each ofthe data types. When selecting a specific data type, the customer canselect to send this data in an anonymized form to the provider or todelete the previously shared data with a particular organization.Additionally, the customer can delete all data types that werepreviously shared with an organization, for example, by clicking on the‘trash’ button provided in the customer interface. According to oneembodiment of the present invention, the deletion of the data mayinclude the customer automation system 300 loading an appropriate scriptfrom the script storage module 320 in order to generate a formal requestto the associated organization to delete the specified data. As notedabove, for example, the customer automation system 300 may be used tomake such request by initiating a communication with a live agent of theorganization or by accessing an application programming interfaceprovided by the organization.

Post-Interaction Automation

Embodiments of the present invention include methods and systems foridentifying outstanding matters or pending actions for a customer thatneed additional attention or follow-up, where those pending actions wereraised during an interaction between the customer and a contact center.Once identified, other embodiments of the present invention includemethods and systems for automating follow-up actions on behalf of thecustomer for moving such pending actions toward a resolution. Forexample, via the automation resources disclosed herein, the personal bot405 may automate subsequent or follow-up actions on behalf of acustomer, where those follow-up actions relate to actions pending from aprevious interaction with a customer service provider. As will beappreciated, this type of automation is primarily aimed at those actionsnormally occurring within the post-contact or post-interaction stage ofa customer interaction, however it also includes the automation ofaction that also can be characterized as preceding or prompting asubsequent customer interaction.

With reference to FIGS. 9, 10 and 11, an exemplary interaction isillustrated of a conversation transcript between a customer and acontact center agent stemming from a call made by the customer todiscuss his internet connection. Using this text, exemplaryfunctionality is provided as to how the interaction may be processed bythe personal bot 405 in order to identify unresolved or pending actionsand/or take follow-up actions for the customer in relation thereto.

With specific reference to FIG. 9, text of the conversation between thecustomer and the agent is presented, which may be referred to as aninteraction 505. It will be appreciated that, while the example includesthe customer interacting with a live agent, the present functionalitycould also be used in situations in which the customer instead interactswith an automated process or chatbot. In the dialogue of the interaction505, the customer is contacting the contact center to discuss a slowinternet connection that he has been experiencing. The conversationprogresses with the agent diagnosing the reason for the slowconnection—the customer using his current plan's allotment of high-speeddata—and then offering the customer an upgraded data plan that includesunlimited high-speed data. The agent tells the customer that the upgradeoffer includes special discounts and free equipment if the customeragrees to enroll for a specified term. Toward the end of the interaction505, the agent discusses with the customer several actions and timeperiods relevant to enrolling the customer in the upgraded plan and howlong it will take for the customer to again have a high-speed dataconnection. The customer then agrees to enroll in the upgraded plan asoffered by the agent.

It will be appreciated that, as the interaction 505 concludes, bothparties to the conversation have agreed upon or suggested (or, as alsoused herein, “promised”) a course of action that includes severalactions being performed in the future. (Note: these actions, as alreadymentioned, will be referenced herein as “pending actions” and,generally, described as being “promised” by one of the parties in theinteraction. It should be understood that the use of the term “promised”is intended broadly and may be used herein to described instances wherea pending action is merely implied or recommended in a party'sstatements, as it may be desirable in certain embodiments to identify apending action for follow-up where no express or binding promise isactually made.) For example, several actions have been promised by theagent (and, by extension, the business or organization the agentrepresents) that need to be fulfilled so that the customer, in fact, isenrolled in the upgraded plan and the high-speed data connection isdelivered to him in the manner promised. As will now be discussed,embodiments of the present invention may effectuate through customerautomation the performance of these pending actions.

In accordance with exemplary embodiments, once an interaction hasconcluded, example embodiments of the present invention (for example,via functionality associated with the personal bot 405 and/or customerautomation system 300) may record and document the interaction for acustomer, e.g., storing data related thereto in the customer profile330. For example, different aspects related to the communication may berecorded and stored, including text, voice, and/or video. The customerautomation system 300 may further document the interaction by storingand indexing any messages, documents, files, and other media involved orshared during in the interaction and, thereby, provide a customer withthe ability to later search this material. As an example, a customer maysearch for keywords spoken by an agent in order to retrieve saved audiothat was spoken near the keywords. As another example, after conductinga technical support call, a customer may have the ability to recall andview an image that was shared and annotated by an agent when explaininghow to set up a particular piece of equipment. A customer may then beable to view specific details of an interaction, such as the timing ofparticular spoken lines in the conversation and what files were beingshared at the time.

In relation to the example interaction 505 of FIG. 9, the personal bot405 may save the conversation as a transcript or recording. If saved asa recording, the recording may later be transformed to text viavoice-to-text transcription. In any case, an interaction transcript ofthe interaction 505 may be created and saved within a database, wherethat database is accessible to the personal bot 405. As will now bediscussed, the personal bot 405 may include analysis tools that, whenapplied to the interaction transcript, facilitate customer-sideautomation.

With specific reference to FIG. 10, the results of a first type ofanalysis are presented. In this case, the personal bot has analyzed thetranscript from the interaction 505 and, from that analysis, recognized,inferred, and/or classified an overall “context” of the interaction andidentified “intents” (i.e., the meaning or intention behind spokenphrases or word groupings), which, as will be seen, then may be used toidentify pending actions. In performing this analysis, any of themethods and systems disclosed herein may be used, including the use ofconventional technologies, as would occur to one of ordinary skill inthe art. For example, the personal bot may have a machine learning orartificial intelligence (AI) engine that is trained with predefined setof intents that are segregated by context. Thus, the personal bot mayfirst determine a reason or context of the customer query, i.e., thecontext. Upon determining the context, the analysis may proceed withsegmenting and classifying the interaction transcript in accordance witha predefined set of intents that correspond to that particular context.The results of this step in the analysis are presented in FIG. 10 in apersonal bot classifications 510 section, which is provided as anannotation to the interaction transcript 505.

More specifically, in relation to the example conversation of theinteraction 505, the analysis of the personal bot may begin bydetermining a context 515, which often is found in an initial segment ofthe exchange in a customer's response to an agent asking the reason forthe call. A trained model may be used to do this. For example, atraining data set (e.g., a data set including data pertaining to priorinteractions between customers and contact centers) may be used to trainan NLP algorithm or model—which also may be referred to as a “contextrecognizer model”—and, once trained, the model may be used to recognizethe context of the interaction 505. In the example provided, because thecustomer has called to express his frustration with his internetconnection being slow, the analysis determines that the context 515 is a“slow internet connection”.

As a next step of the analysis, the personal bot may retrieve apredefined list of intents that corresponds to the context, i.e., “slowinternet connection”. In accordance with the list of intents, thepersonal bot may segment the conversation and classify the segments bythose intents. That is, the analysis continues with personal botchunking or segmenting the conversation based on, for example, keywordsand topics covered within particular portions or sections of theexchange. As shown, one of the segments is the one in which the context515 of the interaction is found, with the other segments being primarilydevoted to different topics within the context 515. Based on the topicor subtopics covered in each of these other segments, the personal botthen may classify each with an intent 520 selected from the predefinedlist. As an example, a trained model may be used to do this. Forexample, a training data set (e.g., a data set including data pertainingto prior interactions between customers and contact centers) may be usedto train an NLP algorithm or model-which also may be referred to as a“intents recognizer model”—and, once trained, the model may be used torecognize an intent for each of the other segments. As shown in theexample of FIG. 10, the intents 520 inferred for the remaining segmentsare referred to as “exhausted high-speed bandwidth”, “selling upgrade topremium plan”, and lastly, “customer accepts premium plan”.

These intents 520 then may be used to identify any unresolved,outstanding, or, as referred to herein, pending actions 525. As usedtherein, a “pending action” is defined as any action that is agreedupon, promised, or otherwise suggested by one of the parties during aninteraction, where the action is to be performed or completed after theinteraction has ended. In regard to the relationship between intents andpending actions, an intent may refer to a broader action or objective,while a pending action within that intent may refer to specific actionsnecessary to make that broader objective happen. Thus, as will bediscussed more below, one of the intents identified in FIG. 10 refers acustomer's desire to enroll in a new upgraded plan. To make this intenthappen, several actions must be taken, some of which may be taken careof during the interaction, with others requiring action afterinteraction has concluded. These actions—which have to be completedafter the interaction is concluded—are the ones that present embodimentsmay classify as pending actions 525.

As will be appreciated, certain pending actions may regularly appear inrelation to certain intents. According to example embodiments, someintents may have a predefined list of pending actions. Like thepredefined list of intents that correspond to a particular context, thepredefined list of pending actions may result in more accuraterecognition of pending actions when a particular intent is identified.

Within an interaction, pending actions may be found in statements madeby either the customer or agent, which may include chatbotrepresentatives in place of each, and may make either party responsiblefor completing the action. It should be appreciated that, in the case ofan agent, a pending action may be identified for actions that will becompleted by other representatives or agents (i.e., not just the agenthimself). That is, a pending action may be identified when the agentsuggests or promises an action that will be handled by anotherrepresentative of the contact center and/or enterprise, business, ororganization associated with the contact center. For example, astatement by an agent saying that the agent will call the customer backat a later time creates an identifiable pending action, which is afuture act performed by the agent of calling the customer back. Asanother example, a statement by an agent promising a service call by atechnician on an upcoming day creates an identifiable pending action,which is the future act performed by the technician of completing theservice call. As mentioned, customer statements can also create pendingactions. For example, if the customer suggests that he will do somethingin the future, such as tells an agent that he will forward certaindocuments to the agent later in the day, this creates an identifiablepending action for the customer.

Returning to the specific example of FIG. 10, the results of theanalysis demonstrate that the exemplary embodiments does not identifyany pending actions 525 in the “exhausted high-speed bandwidth” intentsegment. Similarly, none is identified in the “selling upgrade topremium plan” intent segment. As will be appreciated, these resultsgenerally stem from the fact that, in each case, neither the customernor agent make statements that can be reasonably construed as suggestingor promising the completion of a specified action in the future.However, as will now be discussed, present embodiments may be configuredto identify several such pending actions 525 in the “customer acceptspremium plan” intent segment.

As illustrated, a first identified pending action 525 is referenced as“provide premium plan.” As will be appreciated, with the customeraccepting the upgrade to the premium plan, the agent's statements aboutwhat comes with the premium plan substantially creates a promise toactually provide those services. Such promised future actions may beclassified as pending actions.

As further illustrated, a second identified pending action 525 isreferenced as “provide free equipment”. In this case, the agent makesstatements regarding one or more actions that will be taken after theinteraction is completed in order to provide the customer with certainfree equipment (i.e., a modem and router). These statements are madecontingent on the customer upgrading to the premium plan. Once thecustomer agrees to the upgrade, the agent's statements effectivelybecome a promise, and the future actions related to providing theequipment becomes classified as a pending action.

A third identified pending action 525 is referenced as “restorehigh-speed connection”. In this case, the agent makes statementsregarding one or more future actions that will be taken in regard torestoring the customer's high-speed connection once the customer agreesto the premium plan upgrade. When the customer accepts the upgrade, theagent's statements effectively become a promise, and the future actionsrequired to restore the customer's high-speed connection is classifiedas a pending action.

A fourth such pending action 525 is referenced as “stay enrolled inpremium plan”. In this case, the pending action is on the customer-sideof the interaction. That is, once the customer has accepted the upgrade,he has agreed to provide payment for the services, as necessary, overthe term of the agreement. Thus, once the customer accepts the upgrade,the future action of paying for those services and staying enrolledbecomes classified as a pending action.

With reference now to FIG. 11, with several pending actions 525identified, embodiments of the present invention may proceed withidentifying corresponding target timeframes 530. As used herein, atarget timeframe 530 is a time period or deadline associated with theperformance or fulfilment of the pending action 525 to which itcorresponds. As discussed more below, once identified, a targettimeframe 530 may be used to orchestrate the timing of automatedfollow-up actions by the personal bot on behalf of the customer.

The analysis for identifying a target timeframe may include naturallanguage processing via a trained model or neural network, which mayinclude key word or phrase spotting, particularly within the portion ofthe transcript in which the corresponding pending action 525 isidentified. In example embodiments, however, the present invention mayinclude a target timeframe recognizer model that recognizes relevanttimeframe language associated or used in conjunction with the pendingactions 525. For example, the interaction transcript may be analyzed bya target timeframe recognizer model, with the model outputting a targettimeframe 525 for each previously identified pending action 525. Inaccordance with an exemplary embodiment, a training data set (e.g., adata set including data pertaining to prior interactions betweencustomers and contact centers) is used to train the target timeframerecognizer model.

The present system and methods used to identify target timeframes may beconfigured to recognize several different types, allowing for thenecessary flexible for consistent use across a variety of situations. Aswill now be discussed, these categories may include: a) definite targettimeframes; b) deducible target timeframes; and c) indefinite targettimeframes.

In regard to the category of definite target timeframes, thisclassification includes those timeframes that are stated in theinteraction transcript a straightforward or direct or non-ambiguous way.Examples of these types of target timeframes are found in the followingstatements:

-   -   “Your request will be processed within 24 hours.”    -   “Your payment will be credited in the next 6 hours.”    -   “All our technical experts are currently busy, but we can assign        someone to call you back within the next 2 hours.”    -   “We are still working on the problem and will probably get back        to you in another hour.”    -   “It will take us about 2 hours to verify your documents.”    -   “Your complaint is already filed, and it will be resolve it in        the next 2 business days.”

In each of these examples, a distinct and specific timeframe ismentioned in relation to the performance of some action in the future.System and methods of the present invention may be trained to recognizeand decipher these types such timeframes so that a target timeframe isassigned to each identified pending action.

In regard to the next category, deducible target timeframes, as usedherein, are those timeframes that become clear and distinct with someadditional information. In such cases, the pending action may be clearstated, but the target timeframe is not expressed in a clear ornumerical way, as in the example above. That is, some further learnedintelligence must be applied in these cases to determine an appropriatetarget timeframe for the pending action. Examples of these types oftimeframes are found in the following statements:

-   -   “Please check back during business hours.”    -   “I can check if there are better deals for you during        Thanksgiving holidays.”    -   “Christmas week is the right time for you to check back        regarding this.”    -   “The issue needs one of our skilled technical experts. You can        expect a positive update after the holidays.”        In these examples, while the exact timeframe may not be stated        directly, it may be deduced if the input text is syntactically        handled correctly and/or additional information is acquired. For        example, in the first example, the personal bot may need to        search and find the relevant “business hours” information and,        once this is known, the target timeframe for calling back can be        known. In the next three examples, the personal bot may pinpoint        a specific target timeframe once the dates for the referenced        holidays are known.

In regard to the category of indefinite timeframes, as used herein,these are those timeframes that are defined in accordance with lessdefinitive or vague language. That is, statements made in theinteraction reference to a timeframe, however the language used todescribe that timeframe is open to some interpretation. Examples ofthese types of are found in the following statements:

-   -   “The item will be on stock in few days.”    -   “This whole week is crazy, but just give me more time and I will        get it done.”    -   “The product is currently not in stock. You should call us back        later.”    -   “I will check with my manager and get back to you soon.”        In regard to these examples, the target timeframe recognizer        model may be trained to infer an approximate timeframe or        deadline for completing a pending action given an analysis of        the interaction script. In training the model, the system may        analyze similar contexts to predict appropriate ranges for such        indefinite timeframes. Such models may be further improved when        input is received from the customer to confirm assumptions made        by the model. That is, the customer may be asked to confirm the        timing of a follow-up action by the personal bot. The customer        may choose to modify the deduced timeframe. The modified        timeframe may then be used to update the target timeframe        recognizer model so that the model learns and adepts. This may        be done on a per customer basis or applied more globally. In        ambiguous cases, the target timeframe for a pending action may        be confirmed via a prompt and question to the customer via the        customer device.

Returning to the specific example of FIG. 11, the analysis results inidentifying target timeframes 530 for each of the pending actions 525.Thus, in regard to the first pending action 530—entitled “providepremium plan”—the target timeframe 520 is identified as “2 YEARS”. Thistwo-year period reflects the time the internet provider has promised toprovide the customer with unlimited high-speed data per the premiumplan. In regard to the second pending action 530—entitled “provide freeequipment”—the target timeframe 530 is identified as “2 BUSINESS DAYS”.This period reflects the time in which the agent promised delivery andinstallation of the free modem and router. In regard to the thirdpending action 530—entitled “restore high-speed connection”—the targettimeframe 530 is identified as “20 MINUTES”. This period reflects thetime the agent stated it would take for the customer to again have ahigh-speed connection if he signed up for the premium plan. And,finally, in regard to the fourth pending action 530—entitled “stayenrolled in premium plan”—the target timeframe 530 is identified as “2YEARS”. This period reflects the time that the customer promised to stayenrolled in the premium plan.

According the exemplary embodiments, the personal bot 405 may use theidentified pending actions and target timeframes to automate theperformance of follow-up actions on behalf of the customer. Thefollow-up actions may be actions intended or anticipated to help move apending action toward a resolution or completion. In some embodiments,the personal bot may develop a follow-up workflow in which one or morefollow-up actions are scheduled in relation to the target timeframe.

For example, in accordance with present embodiments, the follow-upaction may take the form of a reminder, notification, inquiry, or offerto help made to the customer pertaining to a promise made to thecustomer by the agent during a previous interaction. As an example, thepersonal bot may provide a voice or text message to the customerstating: “John, Walmart said it would be calling you back todayregarding the return request of your previous order.”

The personal bot may present this type of reminder with other automatedfollow-up actions that the customer can then decide to use. First, thepersonal bot may offer to automate the process of connecting to thecustomer to the enterprise, for example, “John, do you want me to startthis interaction with Walmart for you now?” Second, the personal bot,when possible, may offer to handle the entire interaction for thecustomer, for example, “John, would you like me to handle thisinteraction with Walmart for you?” Third, the personal bot may simplyprovide a link with the initial question that, when activate by thecustomer, calls or otherwise contacts the enterprise to begin aninteraction that the customer then handles, for example, “Activate thefollowing link [CHAT LINK] to begin a chat session with Walmart” or“Activate the following link [PHONE NUMBER LINK] to place a call toWalmart.”

As another example, in accordance with present embodiments, thefollow-up action may take the form of a reminder, notification, inquiry,or offer to help made to the customer pertaining promises made by thecustomer to an agent during a previous interaction. Thus, when thecustomer has mentioned an action for himself in an interaction, thepersonal bot can help by later reminding him of this. For example, thepersonal bot may provide a voice or text message to the customerstating: “John, you told American Express you would get back to themtoday in regard to your expired credit card.” As another example, “John,you needed to send your address proofs to Y bank within 24 hours. Shallwe send it now? Or should I remind you later?” As with the above, thepersonal bot may present this type of reminder with offers relating toother automated follow-up actions that the customer can then decide touse.

Now, with specific reference again to FIG. 11, when this type offunctionality is applied to the example interaction 505, several typesof notifications and offers to help may be communicated to the customer.

For example, after 20 minutes has passed since the end of theinteraction 505, the personal bot may remind the customer that hishigh-speed connection should be restored and then inquire whether thishas happened yet. If it has not happened, other follow-up actionsfacilitating another or subsequent interaction with the contact centermay be suggested and then performed by the personal bot once permissionto do so is received from the customer to do so.

As another example, after two business days have passed since the end ofthe interaction 505, the personal bot may remind the customer that hisfree equipment should have been delivered and installed and then inquireas to whether this has taken place yet. If it has not happened, otherfollow-up actions facilitating another or subsequent interaction withthe contact center may be suggested and then performed by the personalbot once permission to do so is received from the customer.

As another example, near the end of the two-year enrollment term for thepremium plan, the personal bot may remind the customer that the end ofthe term is nearing and inquire as to any actions the customer may wantto take, e.g., provide notice terminating the plan at the end of thespecified term, consider other service options that the personal botcould present to the customer, etc. On the other side, the personal bot,when appropriate, may remind the customer about his obligations underthe premium plan once he accepted it. These may include remindersregarding the due date of the monthly fee or, if the customer beganresearching a new internet provider, remind him of the term remaining onhis current premium plan and/or advise him as to any applicable fees orpenalties for early termination. In this way, the follow-up actions ofthe present invention may serve to both remind the customer of pendingactions from previous interactions and facilitate the fulfilment of theunderlying promised actions.

Personalized Customer Profiles

With reference now to FIGS. 12 and 13, attention will now focus onaspects of the present invention aimed at gathering, maintaining,analyzing, and using customer data and profiles. For example, systemsand methods are disclosed for building highly personalized customerprofiles that facilitate the mining and use of customer data. As will beseen, the customer profiles of the present invention may be used inseveral ways, including implementing personalized customer servicesaimed at improving the customer experience.

By way of background, customer service providers or contact centers havelong maintained data on customers, with data pertaining to a particularcustomer often being stored in a customer profile. Once stored, thisdata then may be used by the contact center to manage certain aspects ofthe customer relationship. For example, contact centers may use customerprofiles to facilitate aspects of incoming interactions. However,conventional customer profiles are often limited in scope, for example,including only basic information about customers, with perhaps somepartial history and preferences. Further, conventional customer profileshave been structure and utilized in ways that have constrainedcustomer-oriented advances. In several ways, which will now be touchedon, conventional systems and methods associated with customer data andprofiles have proved inadequate at providing the level ofpersonalization required to deliver advanced customer-orientedfunctionality.

First, conventional customer profiles are overly static. That is, inconventional systems, customer profiles are not regularly updated and,thus, generally ill-equipped at providing helpful real-time clues as toa particular customer's present situation. What's more, conventionalsystems are often not configured to take into account the most currentor relevant customer data, and this deficiency undermines the usefulnessof customer behavior models and other analytics.

Second, while advances in data collection and analysis have increasedthe amount and variety of data being collected about customers andinteractions with contact centers, conventional systems have failed toleverage this new data abundance into customer centric features. Forexample, large repositories of customer interaction data could beanalyzed to determine predictive insights useful at providingpersonalized customer services, yet conventional systems continue toemphasize the use of such data toward improving contact centerperformance, virtually ignoring the customer experience.

Third, conventional systems also fail to properly aggregate datasources. As will be appreciated, opportunities to make cross-categorydata insights are impeded when different types of customer data aremaintained in separate databases. Further, incomplete customer profilesdegrades the ability of the enterprise to respond to and servicecustomers according to particular needs.

As a result of these and other issues, current data systems related tothe maintenance, analysis, and use of customer data and profiles havebeen unsuccessful at promoting customer-oriented advances in the field.This failure is particularly apparent in those instances where thedelivery of new services involves recognizing or predicting a customer'scurrent status or emotional state.

To address this situation, the present invention discloses improvedsystems and methods for gathering, maintaining, analyzing, and usingcustomer data and profiles. For example, systems and methods aredisclosed for building highly personalized customer profiles thatfacilitate the analysis and mining of customer data. From there, thecustomer profiles of the present invention may be used in several ways,including implementing personalized customer services aimed at improvingthe customer experience and/or removing the interaction “friction” thatnormally occurs between customers and contact centers. On the customerside of the interaction, for example, routing strategies can become morepersonalized in accordance with specific customer preferences and apresent emotional state, thereby making routing more customer centric.On the contact center side of the interaction, the present customerprofiles also may be used toward improving contact center operations,such as, for example: making call forecasting more context oriented andreliable; improving handle time predictions and queue optimization;improving outbound campaigns (e.g., by targeting customers who are morelikely to see value in and respond positively to a particular offer);improving agent assists or automated processes with more customerpersonalization (e.g., by anticipating customer needs to reduce thesteps needed to complete an interaction and/or alleviate need forcustomer to provide information during an interaction); and improvingcustomer communications through greater personalization.

Before proceeding, several terms will first be presented and defined inaccordance with their intended usage. As used herein, “customerexperience” generally refers to the experience a customer has wheninteracting with a customer service provider and, more specifically,refers to the experience a customer has during an interaction, i.e., ashe interacts with a contact center. As used herein, “customer data”refers to any information about a customer that can be gathered andmaintained by a customer service provider. As provided below, suchcustomer data may be categorized with reference to several differentinformation types. In discussing how such data is stored, reference maybe made to a “customer profile” (such as customer profile 330), which,as used herein, refers a collection or linking of data elements relevantto a particular customer. Reference may also be made to “customerdatabases” (such as customer databases 610), which, as used herein,refers to a collection or linking of data elements relevant to orgathered from a large population of customers (or “customerpopulation”). Further, as stated, reference may be made interchangeablyto contact center or customer service provider. It should also beunderstood that, unless otherwise specifically limited, reference to acontact center includes reference to the associated organization orenterprise on behalf of which the customer services are being provided.This includes arrangements in which the associated organization orenterprise is providing the customer services through an inhouse contactcenter as well as arrangements in which a third-party contact centercontracts with the organization or enterprise for providing suchservices.

With specific reference to FIG. 12, an exemplary system 600 is shownthat includes a personal bot 405 running on a customer device 205, wherethe personal bot 405 facilitates the creation and maintenance of apersonalized customer profile database or module (or simply “customerprofile”) 330. As shown in the example, the customer profile 33 mayinclude elements 330A local to the customer device 205 as well as remoteor cloud hosted elements 330B. The system 600 may further includecustomer databases 610, other customer profiles 620, and a predictormodule 625. (Note that further information in regard to the system 600of FIG. 12 may be found above in relation to the system 400 of FIG. 8,which includes several similar features that are not described againhere for brevity's sake.)

For the sake of an example, a customer may have a mobile device or smartphone on which is running an application implementing local aspects ofthe personal bot 405. In setting up a customer profile 330, the personalbot 405 may serve as a means for the customer to input information. Forexample, the personal bot 405 may prompt and accept direct input ofinformation from the customer by voice or text. The customer may alsoupload files to the personal bot 405 or provide the personal bot 405with access to pre-existing databases or other files from whichinformation about the customer may be obtained.

The personal bot 405 also may gather information about the customer bymonitoring customer behavior and actions through the customer's use ofthe device 205. For example, the personal bot 405 may collect data thatrelates to other activities that the customer performs through thedevice, such as email, text, social media, internet usage, etc. Thepersonal bot 405 also may monitor and collect data from each of theinteractions the customer has with customer service providers, such as acontact center system 200, through the customer device 205. In this way,data may be collected from interactions occurring with many differentcontact centers.

In use, at the conclusion of each interaction, the personal bot 405 ofthe present invention may update the profile of the customer inaccordance with data gleamed from that interaction. Such interactiondata may include any of the types of data described herein. As discussedmore below, once the profile is updated, it will include data associatedwith that most recent interaction as well as data from other pastinteractions. This updated or current dataset then may be analyzed inrelation to one or more customer databases 610, which, as used herein,are data repositories housing customer data, such as interaction datarelating to past interactions, from a large population of othercustomers. The analysis may be performed with the predictor module 625,which may include a machine learning algorithm that is configured tofind data driven insights or predictors (or, as used herein,“interaction predictors”).

As used herein, the interaction predictors represent a behavioral factorattributable to the customer given the first interaction type. As willbe seen, the behavioral factor of the interaction predictor may includean emotional state, behavioral tendency, or preference for a particularcustomer given a type of interaction (also “interaction type”). Theinteraction predictor may be generated and applied in real time, forexample, by the predictor module 625. Alternatively, the interactionpredictors may be determined and stored in the customer profile 330 of agiven customer as a way to augment or further personalize the profile.Such stored interaction predictors then may be applied in futureinteractions involving the customer when found relevant thereto. Thepredictor module 625 may be a module within the personal bot 405 or, asillustrated, may be a separate module that communicates with thepersonal bot 405.

Thus, in general, a personal bot 405 may gather relevant information asa customer interacts with contact centers on his mobile device. Thepersonal bot 405 may gather other types of information, as describedabove, and then may aggregate that data to build a highly personalizedcustomer profile 330. As will be appreciated, when a customer profile iscreated and maintained by a contact center, it is generally limited todata pertaining to past interactions occurring between a customer and aparticular contact center. In the present invention, with the customerprofile 330 being created and maintained on the customer-side of theinteraction, the collection of data is not so limited. Instead data maybe gathered from any of the interactions involving the customer, whichwill typically result in a much richer set of data as it reflects awider spectrum of interactions.

The system of FIG. 12 may include a collection of data that is referredto other customer profiles 620. As will be appreciated, when versions ofthe personal bot 405 are used by many customers, data may be anonymouslygleaned from the many corresponding customer profiles 330 (as shownwithin the other customer profiles 620) so to create rich repositoriesof customer data. For example, such data repositories may includeinformation taken from a multitude of past interactions covering a widespectrum of both customers and customer service providers. As indicated,this data may be parsed and aggregated into the customer databases 610so to provide particular datasets that facilitate machine learning andother data driven analytics.

While the customer profiles 330 of the present invention may include anytype of customer data, exemplary embodiments may include several primarycategories of information. These categories include: biographic personaldata (or simply “personal data”); past interaction data (or simply“interaction data”); feedback data; and choice data. As will also beseen, present systems and methods may predict or infer certain behaviortraits, preferences, or tendencies about a customer through dataanalytics. Such predictions—which are introduced above as “interactionpredictors”—may also be stored within a customer profile 330 and thenutilized in subsequent interactions as a way of enhancingpersonalization and facilitating other customer centric features.Alternatively, the interaction predictors may be generatedcontemporaneously and used in relation to an incoming interaction.

It should be appreciated that, while the data stored within the customerprofile 330 may be discussed in categories, the customer profile 330 ofthe present invention may be structured to include an aggregatedcollection of information that may be analyzed as a whole. Further, itshould be understood that the data within a customer profile 330 may bestored locally on a customer device 204, remotely in the cloud, or somecombination thereof. Present systems and methods may further includefunctionality that protects a customer's data from unwanted disclosure.In general, the data stored within the profile of a customer iscontrolled by the customer, with the customer deciding what informationis to be shared during each interaction with an outside organization orenterprise. Thus, before any customer profile data is shared with anoutside entity, such as a contact center or other organization, presentsystems and methods may first seek to confirm with the customer thatsuch sharing is intended. Additional functionality may enable thepartial sharing and use of customer information in ways that maintain acustomer's anonymity.

In regard to the types of data stored within a customer profile 330, afirst category is referred to herein as personal data. This type of datamay include general information about the customer that is generic toall interactions with customer service providers, for example, name,date of birth, address, Social Security number, social media handles,etc. This type of data may also include biographical information, suchas education, profession, family, pets, hobbies, interest, etc. Thiscategory of data may also include data that is specific to particularcontact centers. For example, data related to authentication informationspecific to the different companies that the customer does businesswith, including usernames and passwords, may be included. Such personaldata may be added to a customer profile 330 when a customer isregistering with or setting up the mobile application, i.e., personalbot 405, on his mobile device. For example, a prompt by the personal bot405 may be provided that initiates input of the necessary information.When setting up the mobile application, the customer may be asked via auser interface generated on his customer device for certain information.Once gathered, the personal data of the customer may be made part of thecustomer's profile. The customer may update this information at anytime. As will be seen, aspects of the personal data may be used to findsimilarities with other customers, which may be used when makingpredictions about the customer.

The customer profile 330 of the present invention further may include acategory of information referred to herein as past or historicalinteraction data (or simply “interaction data”). As used herein, thisrefers to data pertaining to or measuring aspects of previous customerinteractions. Accordingly, such data may include a complete historicalrecord of data reflecting all past interaction between a customer andany contact center. Interaction data may include any of the types ofinformation described herein relating to interactions, including type orintent of the interaction, information associated with the dialoguebetween the agent and customer, such as a recording or transcript,information related to the agent, including agent type and othercharacteristics, information about results of the interaction, notesprovided by the customer or the agent, files shared during theinteraction, length of the interaction, call transfers or holds thattook place during the interaction, emotional state of the customer, andothers. The customer profile 330 may be updated after each newinteraction with such interaction data taken therefrom. The interactiondata may further include feedback data and choice data, which arediscussed below.

The customer profile 330 of the present invention further may includefeedback data, which, as used herein, refers to feedback received from acustomer that relates to a particular interaction with a contact center.As will be appreciated, feedback and survey responses may provide avaluable indication as to what went right or wrong in an interaction.Often such feedback is provided by customers at the end of aninteraction in response to surveys or ratings requests. In accordancewith the present invention, any type of feedback, including customersatisfaction score or ratings, provided by a customer at the conclusionof an interaction is saved within a customer profile 330 as feedbackdata. Systems and methods of the present invention may includefunctionality wherein the personal bot 405 gathers such feedback datafor storage within the customer profile 330. The personal bot 405 may dothis via passively recording such feedback when provided by the customerin response to a query initiated by an outside entity, such as a contactcenter. The personal bot 405 also may actively prompt for such feedbackat the end of an interaction and record any responses provided by thecustomer.

Another type of feedback data may include what will be referred toherein as “conclusory statement data”. Conclusionary statement data mayinclude data related to statements made by a customer as the interactionis concluding, where the meaning of the statements is extracted bynatural language processing. Conclusory statement data, thus, may beseen as a type of inferred feedback, i.e., feedback inferred fromstatements made while the interaction is concluding.

For example, the personal bot 405 may gather such conclusory statementdata by analyzing statements or comments made by the customer at theconclusion of an interaction and, where appropriate, inferring customerfeedback from the analysis of those statements. Specifically, suchconclusory statements by the customer may be extracted and analyzed vianatural language processing and, when the customer's statements areclear enough to infer feedback with sufficient confidence, the inferredfeedback may be gathered for storage within the customer profile 330 asa type of feedback or interaction data. As such statements are oftenhighly relevant as to how the customer feels at the conclusion of aninteraction, such inferences can prove useful, particularly where noother rating or survey response is provided by the customer for a giveninteraction. According to exemplary embodiments, for example, suchfeedback data may be used to assist contact centers in deciding on thelevel of service that a customer should receive in a next interaction.

The customer profile 330 of the present invention further may includechoice data, which, as used herein, refers to data that relates to aselection or choice made by the customer in selecting an agent. Morespecifically, choice data refers to automatically learned preferences ofthe customer that are based on the customer's manual selection of oneagent or type of agent over another agent or type of agent. For example,the present invention may include functionality that permits customersto manually choose their own agent from alternatives provided to themvia the customer's computing device. Thus, a customer may be allowed toreview a collection of agent profiles of available agents and thenprompted to select one of those agents to handle the customer's presentor incoming interaction. Alternatively, instead of being presented witha choice between individual agents, the customer may be prompted toselect from different categories or types or agents. The categories, forexample, may describe personality types of the agents. After thecustomer makes several such selections, systems and methods of thepresent invention could begin to learn what type of agent a customermost and least prefers. In example embodiments, such learning can bebolstered by cross-referencing interaction data that describes theactual outcomes of those interactions and/or subsequent feedbackprovided by the customer. In certain cases, this type of analysis mayproduce insights into preferences that even the customer is not fullyaware of having, which may be leveraged to improve predictive routingfor that customer in future interactions.

The data stored within the customer profile 330 of the present inventionmay further include interaction predictors. As used herein, aninteraction predictor is defined as a behavioral characteristic,preference, tendency, or other customer trait that, because ofcorrelations or patterns found to exist within a dataset of relevantcustomer information, can be inferred upon or attributed to a givencustomer. As will be seen, some interaction predictors may be used topredict broad traits, behaviors, or tendencies that are common to manyother customers, while other interaction predictors are highlycontextual and specific to particular type of interaction, such as, forexample, interactions involving a particular intent, emotional state, orcontact center. As will be appreciated, the interaction predictors ofthe present invention offer a way to add detail to a customer profile330 with assumed characteristics that then may be used to personalizeservices and facilitate interactions.

In deriving the interaction predictors, any of the systems and methodsdescribed herein may be used. In exemplary embodiments, as shown in FIG.12, the personal bot is configured to communicate with a predictormodule 625 that includes an artificial intelligence or machine learningalgorithm. As will be appreciated, the machine learning algorithm may beapplied to a dataset of customer information and, therefrom, learnknowledge in the form of data patterns correlating one or more inputfactors to one or more outcomes, with those correlations forming thebasis of the interaction predictors. For example, the machine learningalgorithm in the predictor module 625 may extract such patterns based onmonitored customer actions and associated outcomes. Once such knowledgeis acquired, it may be put to use in the form of the present interactionpredictors to predict outcomes when new inputs are encounters, such asthose presented in an incoming interaction.

Any one or more existing machine learning algorithms may be invoked todo such learning, including without limitation, linear regression,logistic regression, neural network, deep learning, Bayesian network,tree ensembles, and the like. For example, linear regression assumesthat there is a linear relationship between input and output variables,whereas, in the case of neural networks, the learning is done via abackward error propagation where the error is propagated from an outputlayer back to an input layer to adjust corresponding weights of inputsto the input layer.

For the sake of providing examples as to how such interaction predictorsmay be derived for a given customer, reference will now be made to anexemplary customer “Adam”. To begin the process, the machine learningalgorithm of the predictor module 625 may be configured to monitor agiven dataset. This dataset may be obtained from any of the severalsources of data described herein. For example, one or more data sourcesmay be derived from data maintained within Adam's own customer profile(i.e., customer profile 330). The machine learning algorithm may haveaccess to and monitor several of the types of data stored within Adam'scustomer profile, e.g., the personal data, interaction data, feedbackdata, and/or choice data.

For example, to gain insights on what works best for Adam duringinteractions, the machine leaning algorithm could monitor (i.e., use asa training dataset) Adam's interaction data and identify particularfactors that consistently correlate with more successful outcomes. As amore specific example, the machine learning algorithm of the predictormodule 625 may monitor the choice data within Adam's customerprofile—i.e., the agents that Adam selects when given a choice—toidentify patterns relating to the type of agents Adam prefers. Onceidentified, such a pattern could become the basis for an interactionpredictor, which the predictor module 625 would then cause to be storedwithin the Adam's customer profile. When circumstances later arise thatare relevant to the interaction predictor, the interaction predictorcould be recalled from Adam's customer profile and used to facilitatechoices as to how best to provide services to Adam. Specifically, forexample, the interaction predictor could be used to predict which agentout of those available would be most preferable to Adam, as will bediscussed more below in relation to FIG. 14.

In accordance with other aspects of the present invention, the machinelearning algorithm of the predictor module 625 may also monitor andderive datasets from one or more customer databases 610, which, as usedherein, refer to a collection of customer data gathered from “othercustomers”. For example, the customer databases 610 may include datagathered from a large customer population. Such customer databases 610may store any of the customer data types discussed herein and include amultitude of samples collected from a customer population. As anexample, one of the customer databases 610 may include data aggregatedfrom the personalized customer profiles of the present invention, wherethose customer profiles 330 correspond to customers within a customerpopulation (with those customer profiles 330 being represented by thosedepicted within the other customer profiles 620).

In accordance with an exemplary embodiment, the machine learningalgorithm may monitor or derive training datasets from the customerdatabases 610, such as a dataset that includes interaction data takenfrom previous interactions between customers within the customerpopulation and different contact centers. The machine learning algorithmmay then analyze the data within this database to identify patterns inwhich particular factors consistently correlate with certain outcomes.As before, such patterns or correlations may then become the basis foridentifying interaction predictors. Thus, based on similarities found toexist between Adam and the other customers within the customerpopulation, the predictor module 625 may cause one or more interactionpredictors to be applied to or used in connection with Adam.

When identified from a large database of customer information,interaction predictors may be found to be predictively relevant to thecustomer population as a whole or to a group or subpopulation definedwithin the customer population. Thus, in accordance with the presentinvention, the applicability of such interaction predictors to anyparticular customer, such as Adam, may be predicated on a degree ofsimilarity found to exist between Adam and a given subpopulation. Thus,the predictor module 625 may attribute such an interaction predictor toAdam only after determining that a sufficient degree of similarityexists between Adam and the customers within the correspondingsubpopulation or, put another way, whether Adam is determined to bemember of that subpopulation. Upon determining that a sufficient levelof similarity exists between Adam and that subpopulation, the predictormodule 625 may add the particular interaction predictor to Adam'scustomer profile, where it will remain until further machine learningmakes necessitates its modification or removal.

As a general example, a customer database 610 that stores interactiondata may include data collected from interactions between a customerpopulation and many different contact centers. A predictive correlationor other data driven insight—generally referred to herein as aninteraction predictor—is then identified via the machine learningalgorithm of the predictor module 625 by monitoring and analyzing thecustomer database 610. Through this analysis, it may further bedetermined that the identified interaction predictor is only applicableto a particular subpopulation within the customer population. Inaccordance with the present invention, the interaction predictor then isselectively applied to a particular customer if it is determined thatthe customer is a member of the given subpopulation or, at least,sufficiently similar to another customer within the given subpopulation.

Whether gleamed from the customer's own past behavior, based on the pastbehavior of other similar customers, or some combination thereof, oncedetermined, the interaction predictors may be applied to a particularcustomer (for example, saved within his customer profile 330) and thenused to make certain insights or predictions about that customer inorder to enhance aspects of customer service. As will be appreciated,the interaction predictors stored within a customer profile 330 may bedynamically updated as needed so that those currently stored reflectchanges, updates, or additions to the underlying datasets. For example,in an interaction that just concluded, customer Adam made an agentselection that significantly modifies the choice data stored in hiscustomer profile. According to exemplary embodiments, the machinelearning algorithm may continue to monitor Adam's customer profile (andchoice data included therein) and modify the interaction predictors inAdam's customer profile as needed given the modification to theunderlying dataset (i.e., the dataset as modified by his recentinteraction).

Changes to data within the customer databases 610 may also modify howinteraction predictors are applied to Adam. For example, the addition ofnew interaction data within a customer database may modify interactionpredictors that are identified therein. To the extent the modificationimpacts any of the interaction predictors found applicable to Adam,Adam's customer profile would be updated to reflect that. As anotherexample, if Adam inputs new personal information, such as a change inprofessional status or where he lives, existing similarities betweenAdam and certain groups within the customer population may be altered.As those similarities change, the interaction predictors that areattributed to Adam or used in interactions involving Adam will beupdated to reflect the changed similarities.

With the data and the interaction predictors stored in a given customerprofile 330, aspects of the present invention may be used to facilitatethe personalized delivery of customer services related to a present orincoming interaction. For example, contextual information or factorsrelated to the incoming interaction may be identified and, based onthose identified factors, predictions can be made about the customer bydetermining which of the stored interaction predictors are applicable.Alternatively, it should also be understood that such predictions aboutthe customer may be made contemporaneously with the incoming interactionvia the machine learning algorithm (or models developed therefrom)finding similarities in the contextual information around the incominginteraction and past interactions experienced by the customer and/orother similar customers within the customer databases 610. In eithercase, one or more interaction predictors applicable to the incominginteraction may be used to facilitate the delivery of services to thecustomer during the incoming interaction.

In accordance with exemplary embodiments, the relevant interactionpredictors along with any other relevant information from the customerprofile 330 may be packaged within an interaction profile and thendelivered to a contact center for use thereby. As will be seen, thecontact center may then use this package data or interaction profile tofacilitate decisions as to the nature of services that should beprovided to the customer during the incoming interaction. Embodimentswill now be discussed covering exemplary implementations as to how thisinformation may be used. For the sake of these example, reference againmay be made to customer Adam.

In accordance with a first example, systems and methods of the presentinvention may be used to predict a customer's emotional state in theincoming interaction. For example, based on the series of interactionsthat Adam has experienced, interaction predictors may be developed thatrelates such interactions to a pattern of emotional states, which may begleaned from analyzing interaction transcripts for language indicativeof particular emotional states. A customer's emotional state, forexample, may vary in accordance with a predictable pattern that relatesto factors such as: intent of the interaction; recent unsuccessfulefforts to resolve the same issue; unfavorable history with a certainenterprise; etc. By learning these patterns using the systems andmethods disclosed above, it now becomes possible to make predictions asto the emotional state that the customer is likely to exhibit in thenext incoming interaction.

For example, Adam calls Best Buy to enquire about an online order thathe placed last week for an iPhone. Best Buy, as a retailer, answerAdam's question, but tells him that the order was placed with Apple.Best Buy gives Adam with an order identification number and redirectshim to a customer service provider associated with Apple. Adam, nowconnected with Apple, is told by an agent that his order has beenfulfilled and sent to FedEx for shipment. The Apple agent furtherprovides a reference shipping number for tracking the order. With thisnew information, Adam goes to the FedEx webpage, however he finds thatthe tracking information fails to provide any information about hisorder. Adam now calls FedEx to inquire about it. After being on hold forseveral minutes, a FedEx agent finally informs Adam that FedEx has notreceived the requested order from Apple and that the tracking number hehas been provided is incorrect. Adam now instigates anotherinteraction—referred to as an incoming interaction for the sake ofdescribing functionality—with Apple. Each of these interactions are donethrough a customer device of Adam that has a personal bot 405 inaccordance with the present invention.

The personal bot 405 of the present invention may be tracking theinteractions Adam has instigated with the customer service providersassociated with Best Buy, Apple, and FedEx. Using systems and methodsdescribed herein, Adam's customer profile may be updated with each ofthese interactions as they happen and, through natural languageprocessing of transcripts and other available information relating tothe interactions, the personal bot 405 may become aware that: a) thesituation involves several interactions relating to common subjectmatter (i.e., the same problem); b) that Adam has already initiatedseveral recent interactions with different enterprises in an effort toresolve that problem; and c) Adam has so far been unsuccessful and theissue remains unresolved.

To continue the example, the predictor module 625 may have gleamedseveral interaction predictors that are relevant to this situation. Asdescribed above, these may have been determined via analyzing (e.g., byusing a machine learning algorithm) data associated with Adam's own pastbehavior and/or the behavior of a population or group of other customersthat are similar to Adam in ways found to be predictively relevant. Theapplicable interaction predictors, for example, may predict that thesituation is one that likely would induce a particular emotional statefor the customer, such as negative emotions like anger or frustration.Thus, by using information stored within the Adam's customer profile andrecognizing the number and subject matter of Adam's recent interactions,a prediction can be made as to Adam's emotional state coming into theincoming interaction that Adam just initiated with Apple. Specifically,it can be predicted that Adam will likely be angry or frustrated. Thistype of insight then can be used in several ways to tailor the serviceAdam receives once he connects with Apple. For example, as will bediscussed more below, this prediction may be used to select an agentthat is more adept at handling interactions with frustrated or angrycustomers.

Related to the above example, systems and methods of the presentinvention can also be used to facilitate a proactive engagement by acustomer service provider or contact center. That is, given theabove-described pattern of recent interactions logged within Adam'scustomer profile, the personal bot 405 of the present invention canpredict not only that Adam is angry or frustrated, but also that theissue remains unresolved and that Adam will soon be contacting Appleagain as he tries to find a resolution. With these types of predictions,the personal bot 405 can also include functionality whereby a particularenterprise (Apple in this case) is notified that Adam's issue remainsunresolved and Adam will likely be trying to contact Apple again. Thistype of information could then prompt Apple to proactively initiate thenext interaction before Adam does. As will be appreciated, this type ofproactive step by an enterprise would go long way toward repairing acustomer's negative feelings, while also facilitating a resolution to anongoing issue. Which is to say, if it can be predicted that a customer'sissue remains unresolved and the customer is likely to instigate anotherinteraction soon, it may be very favorable from a customer relationshipperspective for the enterprise to be the party that instigates that nextinteraction. With personal bot's extensive customer data coveringmultiple enterprises and multiple intents, these predictions on upcominginteractions can be made and the given enterprises convenientlynotified.

Taking further advantage of the systems and methods disclosed herein,the personal bot 405 may be able to compute a severity rating for anincoming interaction. As used herein, a severity rating for aninteraction is a prediction as to how serious or important aninteraction is to a customer. Conventional contact centers typicallypredict a severity or importance for an incoming interaction based uponthe intent of the interaction. For example, for any incoming interactionwith an intent determined to be “stolen credit card”, a severity ratingof “high severity” (i.e., high level of importance) is allocated. Asanother example, for an incoming interaction with an intent determinedto be “forgotten password”, a severity rating of “moderate severity”(i.e., moderate level of importance) is allocated.

Similar to the process described above in relation to predictingemotional state, present systems and methods may learn to personalizeseverity ratings for particular customers based on the pattern ofinteractions stored in the customer profile 330 and interaction data forsimilar customers. As before, learned interaction predictors may applyspecifically to a particular customer, such as Adam. Along with intent,such interaction predictors may take into account other factors, suchas, for example, time of the day, type of enterprise, recentinteractions, and the emotional state of the customer. With thisinformation, the personal bot 405 can tailor severity ratings forincoming interactions for particular customers. As will be appreciated,different customers may view the same type of interaction with varyinglevels of importance. With the present invention, these varying levelsmay be determined, and service levels varied accordingly.

The systems and methods of the present invention also may be used insimilar ways to make other useful predictions related to incominginteractions, which then may enable improved customer service. Asdiscussed more below, a first of these include using the customerprofile 330 of the present invention to personalize routing decisionsfor customers.

As another example, based on the customer profile 330 (and interactionpredictors stored therewithin) as well as the intent and othercontextual factors related to the incoming interaction, the personal bot405 can make predictions regarding the likelihood of success ofupselling and/or cross-selling opportunities available to the givenenterprise or contact center. As an example, certain customers may bedetermined to be more approachable than others with upselling orcross-selling offers. As another example, a customer's emotional statecould be a factor that is found to correlate with the success ofupselling or cross-selling opportunities. Specifically, an angry orfrustrated state may negatively impact the likely success of attempts toupsell or cross-sell a customer. Indeed, it may be found that, incertain situations, the attempt to upsell or cross-sell such customeronly serves to make the customer angrier or more frustrated. It will beappreciated that contact centers could apply such insights toward makingmore productive routing decisions. For example, those incominginteractions that rate well in regard to upselling or cross-sellingopportunities could be steered to agents that perform better in thisarea.

As another example, the present systems and methods may be used topredict a preferred communication channel for an incoming interaction,with the preferred communication channel being the channel offering thebest chance for successful resolution given the customer. As before,based on the customer profile 330 (and interaction predictors storedtherewithin) as well as the intent and other contextual factors relatedto the incoming interaction, the personal bot 405 can predict apreferred communication channel for initiating an interaction with thecontact center. As another example, if a customer has reached out to hisbank about a forgotten password, the personal bot 405 could redirect theinteraction to a self-service portal which is configured to instantlyresolve this kind of interaction. In this way, the customer can avoidthe wait to be connected with agent that is unnecessary.

With reference now to FIG. 13, a method 650 is shown for personalizing adelivery of services to a customer (which, for clarity, will be referredto as a “first customer”) via a personalized customer profile. The firstcustomer may have a communication device, such as a smart phone, throughwhich interactions with several contact centers are conducted.

As an initial step 655, the method 650 includes the step of providing acustomer profile for storing data related to the first customer.

At a next step 660, the method 650 includes the step of updating thecustomer profile via performing a data collection process to collectinteraction data related to the interactions between the first customerand contact centers. The data collection process may be performedrepetitively so to update the customer profile after each successive oneof the interactions. Described in relation to an exemplary firstinteraction between the first customer and a first contact centers, thedata collection process may include the steps of: monitoring activity ona communication device of the first customer and, therefrom, detectingthe first interaction with the first contact center; identifying datarelating to the first interaction for collecting as the interactiondata; and updating the customer profile to include the interaction dataidentified from the first interaction. The contact centers involved inthe interactions from which the interaction data is collected mayinclude multiple different contact centers.

At a next step 665, the method 650 includes the step of identifying adataset for deriving an interaction predictor. The dataset may be based,at least in part, from the data stored within the customer profile. Morespecifically, the dataset may include the interaction data stored in thecustomer profile.

At a next step 670, the method 650 includes the step of deriving aninteraction predictor by applying a machine learning algorithm to thedataset to identify patterns therein correlating one or more inputfactors to one or more outcomes relevant to the first customer given aparticular type of interaction, which, for the sake of clarity, will bereferenced as a “first interaction type”. As explained more above, theinteraction predictor may be based on knowledge acquired by using amachine learning algorithm to “learn” a set of data or dataset. Theknowledge may relate to a behavioral factor attributable to the firstcustomer when encountering the first interaction type. According toexemplary embodiments, the behavioral factor of the interactionpredictor is defined as an emotional state, behavioral tendency, orpreference. Though other types of machine learning algorithms may alsobe used, exemplary embodiment include a neural network.

At a next step 675, the method 650 includes the step of augmenting thecustomer profile of the first customer by storing therein theinteraction predictor. The storage of the interaction predictor mayinclude linking the behavioral factor to the first interaction type tofacilitate real time retrieval, for example, when for use in relation toa subsequent or incoming interaction that is the same as the firstinteraction type.

At a next step 680, the method 650 includes the step of modifying, inaccordance with the behavioral factor, a manner in which services aredelivered to the customer in an incoming interaction. For example, anincoming interaction instigated by the first customer may be detected asbeing the same as the first interaction type. In response thisdetection, the derived interaction predictor may be retrieved from thecustomer profile of the first customer, and, upon being retrieved, therelevant behavioral factor can be identified. The manner in whichservices are delivered to the first customer in the incoming interactionmay be modified pursuant to the behavior factor. More specifically, onceidentified, the behavior factor may be transmitted to the contact centerinvolved in the incoming interaction. The contact center may then usethe insight provided by the behavior factor to modify the way itdelivers services to the first customer in the incoming interaction.

The method 650 may be performed in accordance with several additional oralternative steps, which provide a range of functionality. Further,significant terminology of the process may be defined so to the basicmethodology yields interaction predictors covering a range ofapplications. Examples of these alternatives will now be discussed.

In accordance with exemplary embodiments, the steps of the datacollection process may be performed by an automated assistant softwareprogram or application, which will be referred simply as “automatedassistant”. The automated assistant may operate on the communicationdevice of the first customer. In example embodiments, the automatedassistant is the personal bot described above. Further, the customerprofile may be stored in cloud-hosted databases, which are updated bythe automated assistant in accordance with the data collection process.As an example, the automated assistant may transmit the collectedinteraction data over a network to the cloud-hosted databases.

In exemplary embodiments, the behavioral factor of the interactionpredictor is an emotional state attributable to the first customer giventhe first interaction type. The emotional state may be represented by atleast one descriptor representative of either a negative emotional stateor a positive emotional state. For example, the emotional state may besimple indicate a satisfied emotional state or an unsatisfied one. Otherexamples include positive emotional states, such as happy, calm, orthankful, and negative emotional states, such as angry, frustrated,confused, sad, or impatient. The interaction data included in thedataset may include data from the interactions evidencing the negativeand positive emotional states. For example, the interaction data mayinclude feedback data related to an evaluation, survey, or satisfactionscore provided by the first customer after a termination of theinteraction. The interaction data may include conclusory statement datarelated to statements made by the first customer as the interaction isconcluding. As described earlier, this type of data may constitute aninferred type of feedback data. The meaning of such statements may beextracted by natural language processing.

When deriving the interaction predictors, the way in which thebehavioral factor and first interaction type are defined may be variedin accordance with a desired functionality. For example, continuing withthe behavioral factor being defined as an emotional state, the firstinteraction type may be defined as interactions having a particularintent. In such an embodiment, the resulting interaction predictorbecomes a customer-specific prediction relating to an emotional state ofthe first customer for an incoming interaction having the particularintent. As another example, the first interaction type may be defined asinteractions involving a particular contact center. In this type ofembodiment, the resulting interaction predictor becomes acustomer-specific prediction relating to an emotional state of the firstcustomer for an incoming interaction involving the particular contactcenter. Related to this embodiment, the process for generating theinteraction predictors may be repeated after successive iterations ofthe data collection process. This repetition may be done until thecustomer profile includes the interaction predictors predicting theemotional state of the first customer for interactions involving each ofthe contact centers that the first customer regular interacts with.

In accordance with exemplary embodiments, the characteristics attributedto the first customer via the interaction predictors may be accessed andmodified by the first customer. For example, the automated assistant maygenerate user interfaces on a display of the communication device of thefirst customer that shows the emotional state data for one or more ofthe contact centers. The display may further prompt the first customerfor input modifying the emotional state in any of the interactionpredictors stored within the customer profile. To continue the example,the automated assistant may receive input from the first customermodifying the emotional state of one of the interaction predictors. Theautomated assistant may then update the emotional state of theinteraction predictor in accordance with the input received from thefirst customer.

In another example, the emotional state of the interaction predictor maycomprise a severity rating, which as explained above, rates a level ofimportance the first customer places on the first interaction type. Withsuch embodiments, the interaction data included in the dataset mayinclude data from each interaction evidencing the level of importancethe first customer placed on it. The level of importance, for example,may be based, at least in part, on an analysis of an interactiontranscript in which usage of words indicative of a high level ofemotionality and/or a low level of emotionality is evaluated. In thiscase, if the first interaction type is defined by a particular intent,the resulting interaction predictor becomes a customer-specificprediction relating to a severity rating the first customer places on anincoming interaction having the particular intent.

Alternatively, the behavioral factor of the interaction predictor may bedefined as a behavioral tendency attributable to the first customergiven the first interaction type. For example, the behavioral tendencymay include an upselling/cross-selling opportunity rating, which rates awillingness of the first customer to consider an upselling orcross-selling offer given the first interaction type. In thisembodiment, the interaction data included in the dataset may includedata from interactions describing unsuccessful upselling orcross-selling offers, successful upsell or cross-selling offers, and/orservice or products purchased by the first customer in relation toupselling or cross-selling offers. As will be appreciated, in this case,if the first interaction type is defined by a particular intent, theresulting interaction predictor becomes a customer-specific predictionrelating to an upselling/cross-selling opportunity rating for the firstcustomer in an incoming interaction having the particular intent.

As another example, the behavioral factor of the interaction predictormay be defined as a preference, e.g., an agent preference, attributableto the first customer given the first interaction type. As describedmore below, the agent preference may include a preferred agentcharacteristic for the first customer given the first interaction type.With such embodiments, the interaction data included in the dataset mayinclude choice data, the choice data including preferred agentcharacteristics derived from selections the first customer makes in theinteractions when allowed to select an agent from among a plurality ofoffered agents. As will be appreciated, in this case, if the firstinteraction type is defined by a particular intent, the resultinginteraction predictor becomes a customer-specific prediction relating toa preferred agent characteristic for the first customer in an incominginteraction having the particular intent.

In alternative embodiments, the method may include providing one or morecustomer databases storing data relating to other customers, such asinteraction data relating to interactions occurring between such othercustomers and contact centers. In such embodiments, the derivation ofthe interaction predictor applicable to the first customer may becompleted in accordance with a different process. For example, theinteraction predictors may be generated by a process that includes thesteps of: identifying a dataset that includes the interaction datastored within the one or more customer databases; deriving the knowledgeof the interaction predictor by applying a machine learning algorithm tothe dataset to identify patterns therein correlating one or more inputfactors to one or more outcomes relevant to a type of customer given thefirst interaction type; and attributing the interaction predictor to thefirst customer based shared similarities between the first customer andthe type of customer. As explained in more detail above, the “type ofcustomer” is representative of a subgroup of the other customers, withthe members of the subgroup having one or more common characteristicsfound to be predictively relevant by the machine learning algorithm inregard to the generated interaction predictor. Further, the step ofattributing the interaction predictor to the first customer may includethe steps of: after the customer profile is updated by a completediteration of the data collection process, identifying data within thecustomer profile relevant to the one or more common characteristics; andconfirming that the one or more common characteristics are possessed bythe customer. For example, the one or more common characteristics mayrelate to one or more respective characteristics stored within thebiographical personal data of the customer profile. Further, in the sameway as described above, the manner in which the behavioral factor andfirst interaction type are defined may be varied to produce similaralternative embodiments.

Personalized Customer Routing

With specific reference to FIGS. 14-16, attention will now focus onaspects of the present invention aimed at improving automated systemsfor routing incoming interactions at a customer service provider.Specifically, systems will be presented that further personalize routingdecisions by leveraging aspects of the customer profile 330 disclosedabove. In this way, customer preferences can be better understood andthen used to facilitate agent routing selections.

By way of background, contact centers have long used automated systemsto desirably route incoming interactions to particular agents. Earlyrouting systems (or “routers”) were primarily intent based. Such systemswould simply match an incoming interaction to an available agent basedon the intent of the query, with the intent being matched to a staticdescription of an agent's skillset. Later, predictive routers weredeveloped that used more sophisticated routing rules and models. Inthese systems, agents were rated in multiple performance categories, andthose ratings were more regularly updated with results from recentlyhandled interactions. The rating categories, for example, might includeperformance data such as first call resolution rate, average handletime, customer satisfaction rate, etc. An agent would be selected notonly on the nature of the query and his skillset, but also onperformance. While this represented an improvement in terms of routingcalls to the best available agents, conventional routers still reliedtoo often on stale data and largely ignore customer characteristics.

Customer profiles eventually were developed so that customer data wasused in the routing process, but the data was limited, overly static,and ineffectively implemented. Advances in data collection soonincreased the amount and variety of data being collected about customersand interactions. Such data could have proved valuable in findinginsights that might prove useful in routing decisions. However, this newabundance of data has never been seriously applied in this way, asconventional routing systems remain focused on making routing decisionsbased on producing favorable business outcomes. In addition,conventional routing systems still do not sufficiently aggregateavailable data into routing models or use newly acquired data toregularly update customer profiles. When there is a lag betweengathering and using customer data, customer models can no longerrepresent present conditions and prediction capabilities suffer.Conventional routers generally fail to take into account many factorsassociated with the customers that, otherwise, could be used to improvethe routing and, thereby, the customer experience. For example, factorsassociated with a customer's emotional makeup and preferences as toagent personality have been largely ignored. As will be seen, the issuescan be addressed by leveraging customer-side personalization to improveinteraction routing.

With specific reference to FIG. 14, a system 700 is shown for usingcustomer data to facilitate personalized routing decisions. As will beappreciated, the system 700 is similar to the previously discussedsystem 600 but has been further developed for use with predictiverouters. The system 700 includes several previously discussed elements,including the personal bot 405, the customer databases 610, the othercustomer profiles 620, the predictor module 625, and the contact centersystem 200, which have been already described in detail. Thus, thepresent discussion will focus on describing how those elements mayparticularly function in relation to a routing implementation as well asintroducing several new system components, which, as shown, include aninteraction profile module 715, agent database 725, agent profiles 730,and a routing engine 735.

In accordance with exemplary embodiments, data driven analytics, such asmachine learning, may be applied to particular datasets—for example,those described above in relation to the personalized customer profile330 and the customer databases 610—in order to improve automated routingof incoming customer interactions. It should be understood that, unlessotherwise specified, the routing systems of the present invention may beimplemented with the same types of customer data presented above inrelation to the customer profile 330, for example, personal data,interaction data, feedback data, and/or choice data. As will be seen,such aggregated and rich data sources may enable more accurate andreliable routing models and produce decisions that are personalized perindividual customers.

As described in detail above, such data may be collected through theimplementation of a personal bot 405 running on a customer device.According to exemplary embodiments, the personal bot 405 may gatherinformation as the customer interacts with different contact centers,and newly acquired data may be used to dynamically update customerprofiles 330 and customer databases 610 in real time.

The predictive routers of the present invention also may use theabove-described interaction predictors to further personalize routingdecisions. That is, the automated routing systems of the presentinvention may be enabled by a customer profile 330 having one or moreinteraction predictors. When utilized in the context of the interactionrouting, interaction predictors may represent data driven insights abouthow contextual factors related to an incoming interaction affect a givencustomer's preferences in regard to selecting an agent. According tocertain embodiments, for example, the interaction predictors may providea score or rating that indicates how much each contextual factor of theincoming interaction affects a customer's agent preferences. Asdescribed more fully above, interaction predictors may be identified viathe predictor module 625 monitoring a dataset associated with a customerprofile 330 of a particular customer and customer databases 610 (which,at least in part, may include date derived from other customer profiles620) for predictive correlations or data patterns. When gleamed from oneof the customer databases 610, interaction predictors may be selectivelyapplied to the particular customer based on similarities between thatcustomer and the other customers represented in the customer database,where those similarities are shown to be predictively relevant.

Further, interaction predictors may be tailored to correspond tospecific types of interactions with particular contextual factors andthen selectively used during future interactions. For example, aninteraction predictor may be identified from available data and thenstored within a customer profile 330. The interaction predictor then maybe recalled from the customer profile 330 and used in relation to anincoming interaction when it is determined to be applicable to theincoming interaction. Alternatively, interaction predictors also may beidentified contemporaneously with an incoming interaction via themachine learning algorithm (or models developed therefrom) findingsimilarities in the contextual information around the incominginteraction and other past interactions experienced by the customerand/or other similar customers.

Accordingly, when used in relation to automated routing systems, aninteraction predictor may relate to learned insights or knowledge aboutthe customer that can be put to use as predictions about agentpreferences, i.e., which agent the customer would rather handle anincoming interaction. To better illustrate how the present invention maybe implemented in a predictive routing context, reference will now bemade to both the system 700 of FIG. 14 as well as a method 800 of FIG.15, which illustrates an exemplary routing process 800.

As indicated in the example shown in FIG. 14, the system 700 may bearranged such that the incoming interaction passes through three stages:the personal bot 405 initiates an incoming interaction and/or determinesinitial interaction data 765, which is transmitted (1) to theinteraction profile module 715; the interaction profile module 715 thengenerates a personalized routing profile 770, which is transmitted (2)to a routing engine 735; and (3) the routing engine 775 generates arouting recommendation 775, which is transmitted (3) to a contact centersystem 200. Further aspects associated with these components areprovided below.

The initial interaction data 765 may include data disclosing at least anintent of the incoming interaction and the contact center system 200 forwhich the interaction is intended. Using this information, the incominginteraction may be classified as being a particular type of interaction.For example, a type of interaction may be defined by a particular typeof intent. This information may be used by the interaction profilemodule 715 to begin building the personalized interaction profile.

As used therein, the personalized routing profile is a collection ofdata specifically tailored to a particular interaction for a particularcustomer. The personalized routing profile, thus, may include specificdata pertaining to the interaction and the customer that is used tofacilitate the routing of interactions in accordance customerpreferences. According to exemplary embodiments, the interaction profilemodule 715 builds the personalized routing profile according topreferred agent characteristics data for the first customer for thegiven type of interaction. The interaction profile module 715 mayinclude (or at least may communicate with) the predictor module 625,which, as described above, has access to data stored in the customerprofile 330 and/or the customer databases 610. The interaction profilemodule 715 may generate the personalized routing profiles consistentwith the several embodiments discussed below.

The routing engine 735 is a logic engine that makes routing decisionsbased on stored algorithms, models, rules, equations or other logic. Asfurther indicated, the routing engine 735 may be a hub that: a) receivesdata from the interaction profile module 715 that relates to theincoming interaction, which may be referred to generally ascustomer-side data or, specifically, as a personalized routing profile770; b) receives data from the contact center system 200 that relates tothe incoming interaction, which may be referred to as contactcenter-side data 785; and c) applies logic to the received data tocalculate a routing recommendation 775. The routing engine 735 may thentransmit the routing recommendation to the contact center system 200 foruse in routing the incoming interaction to a particular agent.

As depicted in FIG. 14, the routing engine 735 is positioned in betweenthe customer-side systems and the contact center system 200. Thisarrangement is consistent with exemplary embodiments in which therouting engine 735 is part of a customer-contact center intermediaryplatform (or simply “intermediary platform”) and maintained as aseparate entity from both the customer-side systems, like the personalbot 405 and the interaction profile module 715, and the contact centersystem 200. The intermediary platform 775 may represent a neutralarbitrator for arriving at a routing recommendation that balancescustomer-side preferences with contact center-side business objectives.Alternatively, the routing engine 735 can be integrated into the contactcenter system 200 or the customer-side systems. In each of these cases,it should be appreciated that, in determining the routingrecommendation, the routing engine 735 takes into the considerationcustomer preferences.

With specific reference now to FIG. 15, a routing method 800 is providedfor selecting an agent to handle an incoming interaction based oncustomer preferences. As will be appreciated, the routing method 800represents the processes that primarily occur within a routing engine,such as routing engine 735, and not the processes that occur within theinteraction prediction module 715, which will be discussed below withreference to FIG. 16. The routing process 800 includes some specificterminology, which will now be defined in accordance with intendedusage:

-   -   “first customer” is a reference customer in an example incoming        interaction;    -   “incoming interaction” is a present communication (i.e., just        instigated) between the first customer and a contact center;    -   “candidate agents” are two or more agents of the contact center        that are available and/or otherwise qualified to handle the        incoming interaction;    -   “agent criteria” are a set of agent criterion, wherein each        agent criterion defines a basis by which a characteristic of an        agent can be rated or evaluated;    -   “preferred agent characteristics data” is data describing the        preferences of the first customer in relation to the agent        criteria;    -   “actual agent characteristics data” is data describing how a        particular one of the candidate agents rates in relation to the        agent criteria;    -   “agent favorableness score” is a score reflecting the        favorableness of a particular one of the candidate agents based        upon how closely the actual agent characteristics data, which        rates the actual characteristics of the particular agent,        matches the preferred agent characteristics data of the first        customer;    -   “evaluation of candidate agents” is an evaluation wherein an        agent favorableness score is calculated for each of the        available agents and, from the relative values of the calculated        agent favorableness scores, a most favored candidate agent is        identified;    -   “favored agent routing recommendation” is a routing        recommendation that the incoming interaction of the first        customer be routed to the most favored candidate agent.

In an example of operation, the routing process 800 may begin at step805 where an incoming interaction is identified. The incominginteraction represents a communication between a customer and thecontact center system 200 that has been initiated, but not connected.For example, the incoming interaction may be a communication justinstigated by a customer (referenced for the example as a “firstcustomer”) over any available communication channel. The incominginteraction may be identified by the interaction profile module 715 byinitial interaction data 765 transmitted to it from the personal bot405. The initial interaction data 765 may include data relating to theincoming interaction that is relevant to the building of thepersonalized routing profile 770. For example, the initial interactiondata 765 may include data about the first customer and data about theinteraction, such as the intent and other contextual factors related tothe incoming interaction. Alternatively, the initial interaction data765 may include very limited information, with additional informationbeing retrieved by the interaction profile module 715 from connecteddatabases, customer profiles, etc.

The routing process 800 continues at step 810 where candidate agents areidentified. As used herein, candidate agents are two or more agents ofthe contact center that are available and/or otherwise qualified tohandle the incoming interaction. In exemplary embodiments, the candidateagents may be identified by the contact center system 200 after a promptis provided. Such prompt may be provided by the routing engine 735 uponidentifying the incoming interaction (for example, upon receiving thepersonalized routing profile 770 from the interaction predictor module.Such prompt may include information that is necessary for the contactcenter system 200 to identify the candidate agents, such as, the intentof the incoming interaction.

The routing process 800 continues at step 815 where the preferred agentcharacteristics data for an agent is identified for the first customer.As used herein, preferred agent characteristics data is informationdescribing the preferences of the first customer in relation to theagent criteria. As stated in the definitions above, the agent criteriaare a set of agent criterion, where each criterion defines a basis bywhich a characteristic of an agent can be rated or evaluated. Forexample, an agent criterion may evaluate an aspect of an agent'spersonality, such as whether the agent is more playful or more serious.The preferred agent characteristics data may reflect that, for theincoming interaction, the first customer prefers a more playful agent.

In accordance with exemplary embodiments, the preferred agentcharacteristics data may be included within the data transmitted by theinteraction profile module 715 in the personalized interaction profile.As discussed more below, the preferred agent characteristics data may bedetermined by the interaction profile module 715 and may includepredictions as to the preferences of the first customer. With access todata relating to each of the first customer's past interactions andother activities performed via the first customer's mobile device, thecustomer-side systems (e.g., the personal bot 405, interaction profilemodule 715, and the predictor module 625) are well-positioned to makedata driven inferences that provide the basis for such predictions. Aswill be seen, such predictions may be based on the intent of theincoming interaction and/or other contextual factors relating to theincoming interaction. For example, such predictions may be based on theapplicability of one or more interaction predictors stored within thecustomer profile, such as those related to an emotional state of thecustomer or severity rating of the interaction.

The routing process 800 continues at step 820 where the actual agentcharacteristics data for each of the candidate agents is identified. Asused herein, actual agent characteristics data is data describing how aparticular candidate agent rates in relation to the agent criteria. Aswill be appreciated, the actual agent characteristics data for each ofthe agents may be stored within agent profiles 730 maintained in theagent database 725 of the contact center system 200. The actual agentcharacteristics data may be part of the contact center-side data 785that is transmitted by the contact center system 200 to the routingengine 735.

The routing process 800 continues by evaluating the candidate agents. Asdescribed more below, the evaluation of the candidate agents may includetwo steps: a) calculating an agent favorableness score for each of theavailable agents (step 825); and b) identifying, from the relativevalues of the calculated scores, a most favored candidate agent (step830).

Thus, at step 825, the routing process 800 proceeds by calculating agentfavorableness scores for each of the candidate agents. An agentfavorableness score is a score reflecting the favorableness ofparticular one of the candidate agents based upon how closely the actualagent characteristics data, which describes the actual characteristicsof the particular agent, matches the preferred agent characteristicsdata of the first customer. As will be appreciated, this may beaccomplished using a variety of different scoring systems andalgorithms. In general, the candidate agents will be scored via theagent criteria, with the score for each being indicative of how closelythe actual agent characteristics data satisfies the preferred agentcharacteristics data of the first customer. For example, the agentfavorableness score may be a cumulative score that reflects how well aparticular agent scores in regard to all of the agent criteria. Asanother example, the scoring system may be a weighted one that placesgreater importance to certain agent criterion over others. In suchcases, the weights applied to each category may be the same for allcustomers. Alternatively, the way the weights are applied could bevaried between customers and, thus, be used as another mechanism toexpress different customer preferences.

At step 830, as stated above, the routing process 800 proceeds byidentifying a most favored candidate agent based on the calculated agentfavorableness scores. As will be appreciated, this step may be performedby comparing the relative values of the calculated agent favorablenessscores and determining which is the best and, thus, reflective of a mostfavored agent given the first customer's preferences.

The routing process 800 then continues at step 835 where a routingrecommendation is issued. As will be appreciated, the routingrecommendation may be that the incoming interaction is routed to themost favored candidate agent. As shown in FIG. 14, this step may includethe routing engine 735 transmitting the routing recommendation 775 tothe contact center system 200.

With continued reference to routing processes and concepts introducedabove in relation to FIGS. 14 and 15, attention will turn to severalrouting examples, which, in accordance with the present invention, arepersonalized pursuant to interaction predictors, contextual factors, andother data stored within a customer profile 330. These examples willreference example customers “Jane” and “Betty”. It should be appreciatedthat these examples have been simplified so to clearly illustratesintended functionality.

A first example involves a case where the routing decision ispersonalized, at least in part, pursuant to personal data maintained inthe customer profiles 330. Jane is a business class passenger withDelta. Jane initiates an interaction with Delta and is connected tospeak with “Agent Dave”. Jane has initiated the interaction via using amobile application on her smart phone, which includes theabove-described personal bot 405. The topic of the interaction is Jane'sfrequent flyer miles, which Jane has noticed were wrongly updated in heraccount. From Jane's perspective, the conversation she has with AgentDave goes exceedingly well. Agent Dave rates as having averageproficiency in terms of being knowledgeable and efficient in handlingcustomer issues, but is rated as being funny, playful, and easy to talkwith. The conversation goes so well that Jane completely forgets aboutthe original mistake that prompted her to make the call and, indeed, isreceptive to making an additional (upsell) purchase that was suggestedby Agent Dave. Specifically, after noting how well the conversation hadprogressed, Agent Dave points out that if Jane purchases more frequentflyer miles during an ongoing Delta promotion, she would receive a freecompanion ticket and be promoted to a higher level in the Delta'sfrequent flyer program. All the information related to this interactionis gathered by the personal bot 405 and stored with Jane's customerprofile. The information may also be anonymously used in the customerdatabases 610.

A new interaction is later initiated by a different customer “Betty”,who is also a business class passenger, but for United. Betty is callingfor a similar reason as Jane related to a discrepancy with her frequentflyer miles.

Betty's customer profile has limited interaction data that is relevantto the intent of this incoming interaction. However, the predictivemodule has found sufficient similarities between the personal data ofBetty and that the personal data of a group of other customers withinthe customer database, which includes Jane, that previous routingsuccesses associated with those customers are determined as likely beinginstructive in how to route Betty's incoming interaction.

Thus, for example, aspects of Jane's previous interaction may be used tocreate an interaction predictor that is implemented within Betty'scustomer profile and used to facilitate a routing decision in Betty'sincoming interaction. The interaction predictor, for example, maypredict that Betty should be routed to an agent who is similar to AgentDave of Delta (i.e., funny, playful, etc.). An additional interactionpredictor may be applied in which the upselling opportunity for Betty'sincoming interaction is predicted. For example, generally, when acustomer is calling about an issue, the upselling opportunity may bedeemed as poor. However, in relation to this interaction, theinteraction predictor for upselling may be rate the opportunity asbetter than would be generally anticipated based, at least in part, onthe successful upsell that occurred in Jane's previous interaction withAgent Dave. With such interaction predictors gleamed from specificcustomer profiles and included within a personalized routing profile,the routing engine would score some agents better than others and,thereby, arrive at a routing recommendation that is likely to improvethe overall experience for Betty as well as the potential businessoutcome for the airline.

A second example involves a case where the routing decision ispersonalized, at least in part, pursuant to interaction data maintainedin the customer profiles 330. As previously described, with all the pastinteractions between different customers and various enterprises, thepersonal bot 405 and/or predictor module 625 of the present inventioncould mine the customer databases 610 to determined predictivecorrelations that are instructive as to a customer's presentexpectations and needs. With this “learned” knowledge, a level ofservice could then be delivered that is anticipated to satisfy theexpectations of a customer related to an incoming interaction. Also,such knowledge could provide customer service providers with the abilityto reach out to customers and fill expectations or needs proactively.

For example, consider another example involving Jane and Betty. Jane hasan interaction with Delta, wherein it is learned that she is travellingto Barcelona. Jane also has an interaction with a hotel in Barcelona,which is a Marriott, in which she books a room. The data from these pastinteractions may be monitored and recorded by the personal bot 405 andsaved within Jane's customer profile. Such interaction data may be minedby the personal bot 405 and added to the general customer database 610and/or implemented as an interaction predictor within Betty's customerprofile based, as in the example above, on similarities between Bettyand Jane/other similar customers within the customer database.

Now, when Betty initiates an incoming interaction, and it is determinedthat, like Jane, Betty is a business class traveler who is travelling toBarcelona and looking to book a hotel, several interaction predictorsmay be used to personalize the routing. For example, an interactionpredictor may be used to recommend an agent type for Betty based on anagent that was successful in handling Jane's booking. As anotherexample, an interaction predictor may be used to recommend an agent typewho is knowledgeable in a range of hotels that includes ones similar tothe Marriott that Jane booked. Further, given the knowledge acquiredabout Betty's intent, Marriott and/or hotels similar to Marriott may beallowed to reach out to Betty proactively, for example, to inform herabout upcoming booking deals in Barcelona.

A third example involves a case where the routing decision ispersonalized, at least in part, pursuant to feedback data maintained inthe customer profiles 330. As previously described, the personal bot 405of the present invention may collect feedback and survey detailswhenever provided by customers. The personal bot can understand if acustomer is satisfied with a resolution provided by an agent not only bya customer satisfaction score, but also through data driven modelstrained to automatically infer such feedback from the statements made bythe customer at the end of the interaction. Such feedback providesvaluable data on whether the agent works well with this type of customerprofile 330, which can be mined so that better routing decision can beachieved for further interaction involving similar customers, similarcontexts, and/or similar agents.

For example, consider another example involving Jane and Betty. Jane hasspoken with Delta about a family emergency requiring her to cancel aflight that she had booked already. To do this, Jane is again connectedwith a funny and playful agent, like Agent Dave, who takes too long toget to the point and is too playful given Jane's present agitated andworried mood. For this particular interaction given the contextualfactors, the choice of agents has led to a frustrating experience forJane. Jane later provides feedback as to this experience, which is addedto Jane's customer profile. As before, the feedback may be mined by thepersonal bot 405 and added to general customer database and/orimplemented as an interaction predictor within Betty's customer profilebased, as in the examples above, on similarities between Betty and Janeas well as other similar customers.

Later a new interaction is instigated by Betty, who is calling for asimilar reason as Jane involving a change in travel plans due to afamily emergency. In a manner similar to the other examples, thepredictive correlations gleamed from Jane and other customers may beapplied to Betty based on similarities found to predictive between Bettyand Jane/other customers. Thus, at least in part, useful feedbackprovided by Jane may be used in determining a routing recommendation forBetty. One or more interaction predictors may be implemented in therouting of Betty's incoming interaction that, for example, recommend agreater likelihood of success with a routing to an agent who is moreserious, understanding, and direct or “to the point”.

A fourth example involves a case where the routing decision ispersonalized, at least in part, pursuant to “choice data” maintained inthe customer profiles 330. Generally, when dealing with a contactcenter, customers do not have the ability to choose an agent or type ofagent for their interaction. For example, customers are not allowed toreview a group of agent profiles and select the one that seems mostappealing. As will be appreciated, if this were allowed, the choice datareflecting the choices made by a customer would provide valuable insightas to the type of agents a customer prefers. Such insights could beapplied to other customers based on similarities found predictivelyrelevant.

In accordance with embodiments of the present invention, the personalbot 405 can provide agent summary profiles to a customer and allow thecustomer to select. The agent summary profiles may include certaininformation gleaned from the agent profiles 730 maintained at thecontact center system 200, which may be transmitted to the personal bot405 upon request. For example, an agent summary profile may include apicture of the agent and a short description of the agent's personality.

Initially, the choice data acquired from a customer's choices mayprovide a greater level of understanding as to particular preferences inan agent. As time goes by, though, predictions in regard to a particularcustomer may be made even when contextual factors to an interaction arenew, for example, a new intent or when dealing with a new enterprise.Further, choice data for one customer may be used to make predictionsabout other customer base on similarities therebetween. Specifically,the predictions based on the choice data of a group of customers may befound applicable to another customer based on similarities found betweenthe group and the other customer. As such customer choice data isobtained and updated in customer profiles 330, it may provide the basisfor determining interaction predictors, which ultimately may be usedwithin the personalize routing profiles to calculate agent favorablenessratings and influence subsequent routing decisions.

For example, if Jane constantly chooses to speak with a funny orentertaining agent for retail queries, the accumulated choice data canbe used to influence not only the routing of Jane's future interactions,but also the routing of similar interactions of similar customers, suchas Betty.

As will be appreciated, the present invention may include many differentkinds of interaction predictors, which are applicable to specificinteractions and contexts and helpful in personalizing routing decisionsfor individual customers. These interaction predictors include thosethat were just discussed in relation to the “Jane” and “Betty” examplesbut may also include those discussed above in the “Adam” examples ofFIG. 12 that introduced personalized customer profiles. As will berecalled, that discussion provided details relating to interactionpredictors involving emotional state, severity rating, andupselling/cross-selling opportunity. These will be touched on again inthe context of predictive routing.

By analyzing the pattern of the interactions made by the customer acrossdifferent contact centers, the systems of the present invention may beable to predict an emotional state of a customer coming into an incominginteraction and/or a severity rating for an interaction. For example, inan accident situation, a customer may have placed several previous callsto emergency services and insurance providers. Using the systems andmethods already described, these previous interactions may be recognizedby the personal bot 405 and used to predict an emotional state for thecustomer. Further, the personal bot 405 may be able to predict aseverity rating for the interaction (i.e., how important the interactionis to the customer). As will be appreciated, in this case the severityrating would be predicted as being high or very severe. In either case,one or more interaction predictors could provide these predictions to arouting engine for use in the routing of the next interaction instigatedby the customer. For example, in this case, the one or more interactionpredictors used in building a personalized interaction profile mayrecommend that incoming interaction be routed to an agent who isserious, understanding, and adept at handling emotional customers.

In similar fashion, other interaction predictors can be used to providecontextual insights that produce better routing decisions. For example,interaction predictors may be configured to provide insights as toupselling or cross-selling opportunities. In this case, if the systemsand methods described herein predict that the upselling or cross-sellingopportunity of an incoming interaction is good, the interaction may besteered toward agents that are skilled in this area.

With reference now to FIG. 16, a method 900 is shown for personalizingthe routing of an interaction initiated by a particular customer, which,again, will be referenced as a “first customer”. In accordance withexemplary embodiments, the method 900 facilitates such routing inaccordance with customer preferences. In presenting this aspect of thepresent invention, continued reference is made to FIGS. 13 and 15, asthe method 900 draws on several of the ideas and concepts introducedabove in relation to the methods 650 and 800, respectively. Further,continued reference is made to FIG. 14, which includes exemplary systemsand components for performing the steps of methods, such as the oneshown in FIG. 16.

The method 900 may begin at step 905 by receiving initial dataidentifying an incoming interaction initiated by the first customer.According to exemplary embodiments, the initial interaction data 765 mayinclude data disclosing at least an intent of the incoming interactionand the contact center system 200 for which the interaction is intended.Using this information, the incoming interaction may be classified asbeing a particular type of interaction, which is referred to generallyherein as a given type of interaction or interaction type. For example,a type of interaction may be defined by a particular type of intent. Inregard to the exemplary system 700 of FIG. 14, the initial interactiondata 765 may be sent by the personal bot 405 and received by theinteraction profile module 715.

At a next step 910, the method 900 proceeds by generating a personalizedrouting profile that includes preferred agent characteristics data forthe first customer for the given type of interaction. As described, thisstep may be performed by the interaction profile module 715, whichincludes (or at least communicates with) a predictor module 625 incommunication with or having access to data stored in the customerprofile 330 and/or the customer databases 610. According to exemplaryembodiments, the performance of step 910 may include several actions,which are indicated in FIG. 16 as the broken-out subprocess of steps911-914. This subprocess—which will be referred to as a “first process”and discussed more below—may include the following steps. At a step 911,the first process beings by identifying a dataset that includesinteraction data from the customer profile of the first customer. At astep 912, the first process includes applying a machine learningalgorithm across the dataset to identify patterns correlating actualagent characteristics to a desired outcome for the given type ofinteraction. At a step 913, the first process includes basing thepreferred agent characteristics data for the incoming interaction on theactual agent characteristics data found to correlate to the desiredoutcome. And, at a step 914, the first process concludes by generatingthe personalized routing profile so to include the preferred agentcharacteristics data of the first customer.

Once the first process is performed, the method 900 proceeds to a step915, which, as shown in FIG. 14, includes transmitting the personalizedrouting profile 770 to a routing engine 735. From there, the method 900proceeds to a step 920, where a routing recommendation for the incominginteraction is derived based, at least in part, on the preferred agentcharacteristics data of the first customer. Finally, as also shown inFIG. 14, at a step 925, the method 900 concludes by transmitting therouting recommendation 775 to a contact center system 200 for use inrouting the incoming interaction.

The method 900 may be performed in accordance with several additional oralternative steps, which, as will now be discussed, provide a range offunctionality. Further, as with the method 650 of FIG. 13, theterminology used in describing the method 900 may be defined inaccordance with several alternative uses to cover a range of differentapplications, as provided in the following examples.

As already described, the first process is the process by which thepersonalized routing profile is generated. According to exemplaryembodiments, the personalized routing profile is a profile specificallytailored to a particular interaction for a particular customer. Thepersonalized routing profile, thus, may include specific data pertainingto the interaction and the customer that may be used to facilitate therouting of interactions in accordance with one or more preferences ofthe first customer. With this in mind, the first process may generallyinclude the steps of: a) accessing data from a database that includes atleast a first customer profile storing data relating to the firstcustomer; b) based on the accessed data and the intent of the firstincoming interaction, determining preferred agent characteristics dataof the first customer for the first incoming interaction; and c)generating the personalized routing profile so to include the preferredagent characteristics data of the first customer. Within this generalmethodology, several other specific embodiments of the first processwill now be described.

As a first embodiment, the data used by the first process includesinteraction data relating to past interactions that occurred between thefirst customer and a plurality of the contact centers. With thisembodiment, the first process includes the steps of: a) identifying,from the past interactions of the first customer, qualifying examples inwhich an intent matches the intent of the first incoming interaction,and a desired outcome is achieved; b) determining one or moreconsistencies across the actual agent characteristics data of the agentsin the qualifying examples of the past interactions; and c) basing thepreferred agent characteristics data for the first incoming interactionon the one or more consistencies across the actual agent characteristicsdata.

As a second embodiment, the data used by the first process againincludes interaction data relating to past interactions that occurredbetween the first customer and a plurality of the contact centers. Withthis embodiment, the first process include the steps of: a) applying amachine learning algorithm across the interaction data from the pastinteractions to identify patterns correlating one or more factors to adesired outcome for a given type of interaction, wherein, in performingthis step: the one or more factors may be defined as the actual agentcharacteristics data of the agents in the past interactions; the desiredoutcome may be defined as ones of the past interactions receivingpositive customer feedback or achieving a successful resolution; thetype of interaction is defined as an interaction having an intent thatmatches the intent of the first incoming interaction; and b) basing thepreferred agent characteristics data for the first incoming interactionon the actual agent characteristics data found to correlate to thedesired outcome. As with other embodiments, the machine learningalgorithm may include a neural network.

A third embodiment may include the first customer and a plurality ofother customers (or “other customers”). The data used by the firstprocess may further include one or more customer databases that includeinteraction data relating to past interactions that occurred between theother customers and a plurality of the contact centers. With thisembodiment, the first process includes the steps of: a) identifying,from the past interactions of the other customers, qualifying examplesin which an intent matches the intent of the first incoming interaction;a desired outcome is achieved; and a predetermined similarity is foundto exist between the first customer and a particular one of the othercustomers involved in the past interaction; b) determining one or moreconsistencies across the actual agent characteristics data of the agentsin the qualifying examples of the past interactions; and c) basing thepreferred agent characteristics data for the first incoming interactionon the one or more consistencies across the actual agent characteristicsdata.

In a fourth embodiment, the first process may again use data derivedfrom one or more customer databases that include interaction datarelating to past interactions that occurred between the other customersand a plurality of the contact centers. With this embodiment, the firstprocess includes the steps of: a) applying a machine learning algorithmacross the interaction data from the past interactions to identifypatterns correlating one or more factors to a desired outcome relevantto a type of customer given the type of interaction, wherein, inperforming this step: the one or more factors is defined as the actualagent characteristics data of the agents in the past interactions; thedesired outcome is defined as ones of the past interactions receivingpositive customer feedback or achieving a successful resolution; thetype of interaction is defined as an interaction having an intent thatmatches the intent of the first incoming interaction; and the type ofcustomer is defined as one that shares one or more commoncharacteristics with the first customer; and b) basing the preferredagent characteristics data for the first incoming interaction on theactual agent characteristics data found to correlate to the desiredoutcome. In addition, the customer profile and the one or more customerdatabases may store biographical personal data relating to the firstcustomer and the other customers, respectively. The one or more commoncharacteristics may include one or more characteristics stored withinthe biographical personal data.

In a fifth embodiment, the interaction data of the other customers mayinclude feedback data, with the feedback data including an evaluationprovided by a particular one of the other customers in relation to aparticular one of the past interactions. With this embodiment, the firstprocess includes the steps of: a) applying a machine learning algorithmacross the interaction data from the past interactions to identifypatterns correlating one or more factors to a desired outcome relevantto a type of customer given a type of interaction, wherein, inperforming this step: the one or more factors is defined as the actualagent characteristics data of the agents in the past interactions; thedesired outcome is defined as ones of the past interactions wherein thefeedback data is classified as being positive; the type of interactionis defined as an interaction having an intent that matches the intent ofthe first incoming interaction; and the type of customer is defined by ashared similarity with the first customer; and b) basing the preferredagent characteristics data for the first incoming interaction on theactual agent characteristics data found to correlate to the desiredoutcome. The feedback data may include feedback inferred from one ormore statements made by the other customers within a concluding portionof the past interactions, which is also discussed above as conclusorystatement data.

In a sixth embodiment, the interaction data of the other customers mayinclude choice data, with the choice data including data relating toselections the other customers make when allowed to select an agent (or“selected agent”) from a plurality of offered agents. With thisembodiment, the first process includes the steps of: a) applying amachine learning algorithm across the interaction data from the pastinteractions to identify patterns correlating one or more factors to adesired outcome relevant to a type of customer given a type ofinteraction, wherein, in performing this step: the one or more factorsis defined as the actual agent characteristics data of the selectedagents; the desired outcome is defined as ones of the past interactionsreceiving positive customer feedback or achieving a successfulresolution; the type of interaction is defined as an interaction havingan intent that matches the intent of the first incoming interaction; andthe type of customer is defined as one that shares a predeterminedsimilarity with the first customer; and b) basing the preferred agentcharacteristics data for the first incoming interaction on the actualagent characteristics data found to correlate to the desired outcome.

In a seventh embodiment, the first process may include the steps of:determining one or more contextual factors relating to the incominginteraction by finding relationships between the initial data and datastored within the customer profile of the first customer; and based onthe one or more contextual factors, determining an interactionpredictor, the interaction predictor comprising a prediction applicableto the first customer for the incoming interaction. In this case, thedetermination of the preferred agent characteristics data of the firstcustomer for the first incoming interaction is also based on theinteraction predictor. The interaction predictor may include a predictedupselling/cross-selling opportunity rating. The predictedupselling/cross-selling opportunity rating is defined as a receptivenessof the first customer to considering offers made by the agent inrelation to upselling or cross-selling during the first incominginteraction. Alternatively, the interaction predictor may include aseverity rating. The severity rating predicting a level of importancethat the first customer ascribes to the first incoming interaction. Asanother alternative, the interaction predictor may include a predictedemotional state. In this case, for example, the one or more contextualfactors may include a minimum number (e.g., at least two) of the pastinteractions of the first customer being instigated by the firstcustomer within a predetermined lookback period (e.g., less than 6hours). The predetermined lookback period may be measured from when thefirst customer instigates the first incoming interaction. The pastinteractions instigated by the first customer within the predeterminedlookback each may include: a subject matter that matches a subjectmatter of the first incoming interaction; and an unsuccessful attempt atresolving a customer problem relating to the subject matter. In suchcases, the predicted emotional state may include a negative emotionalstate.

Though not necessary to practice aspects of the present invention,certain embodiments include the performance of additional or downstreamsteps whereby the personalized routing profile, as generated by thefirst process, is utilized in some way. Several of these alternativeswill now be presented in which these downstream steps will be referenceas a “second process”.

For example, the present invention may include a second process thatsimply includes deriving the routing recommendation based on thepreferred agent characteristics data of the first customer. As analternative, the personalized routing profile may be transmitting the toa routing engine for use in deriving a routing recommendation for thefirst incoming interaction. In another example, the second processincludes the steps of: a) deriving the routing recommendation for thefirst incoming interaction based on the preferred agent characteristicsdata of the first customer; and b) transmitting the routingrecommendation to a contact center for routing the first incominginteraction in accordance therewith.

Another exemplary embodiment includes a second process that generates arouting recommendation based on the personalized routing profile. Inthis case, the second process may include the steps of: receiving dataidentifying candidate agents, the candidate agents comprising at leasttwo of the agents of the first contact center that qualify for handlingthe incoming interaction; receiving actual agent characteristics datafor each of the candidate agents; calculating an agent favorablenessscore for each of the candidate agents, the agent favorableness scorecomprising a mathematical representation indicative of how closely amatch is between the actual agent characteristics data for a particularone of the candidate agents and the preferred agent characteristics dataof the first customer; identifying a most favored candidate agent as aone of the candidate agents producing the agent favorable scoreindicating a closest match between the actual agent characteristics dataand the preferred agent characteristics data; and generating a routingrecommendation that recommends routing the first incoming interaction tothe most favored candidate agent. The second process may further includethe step of causing the first incoming interaction to be routed inaccordance with the routing recommendation. Alternatively, the secondprocess may further include the step of transmitting the routingrecommendation to the first contact center for use thereby in routingthe first incoming interaction.

In other exemplary embodiments, the second process includes receivinginformation from the contact center to generate the routingrecommendation. For example, in this case, the second process mayinclude the steps of: a) receiving contact center-side data from thefirst contact center relating to the first incoming interaction, thecontact center-side data disclosing at least: candidate agents, thecandidate agents comprising at least two of the agents of the firstcontact center that qualify for handling the incoming interaction;actual agent characteristics data for each of the candidate agents; andpreferred agent characteristics data of the first contact center, thepreferred agent characteristics data comprising data that describespreferences of the first contact center in relation to the agentcriteria based on a contact center performance objective; b) generatingcombined preferred agent characteristics data by combining via aweighted average the preferred agent characteristics data of the firstcustomer and the preferred agent characteristics data of the firstcontact center; c) calculating an agent favorableness score for each ofthe candidate agents, the agent favorableness score comprising amathematical representation indicative of how closely a match is betweenthe actual agent characteristics data for a particular one of thecandidate agents and the combined preferred agent characteristics data;d) identifying a most favored candidate agent as a one of the candidateagents producing the agent favorable score indicating a closest matchbetween the actual agent characteristics data and the combined preferredagent characteristics data; e) generating the routing recommendationthat recommends routing the first incoming interaction to the mostfavored candidate agent; and f) transmitting the routing recommendationto the first contact center.

As one of skill in the art will appreciate, the many varying featuresand configurations described above in relation to the several exemplaryembodiments may be further selectively applied to form the otherpossible embodiments of the present invention. For the sake of brevityand taking into account the abilities of one of ordinary skill in theart, each of the possible iterations is not provided or discussed indetail, though all combinations and possible embodiments embraced by theseveral claims below or otherwise are intended to be part of the instantapplication. In addition, from the above description of severalexemplary embodiments of the invention, those skilled in the art willperceive improvements, changes and modifications. Such improvements,changes and modifications within the skill of the art are also intendedto be covered by the appended claims. Further, it should be apparentthat the foregoing relates only to the described embodiments of thepresent application and that numerous changes and modifications may bemade herein without departing from the spirit and scope of the presentapplication as defined by the following claims and the equivalentsthereof.

That which is claimed:
 1. A computer-implemented method forpersonalizing a delivery of services to a first customer, the firstcustomer having a communication device through which the first customerconducts interactions with contact centers, the method comprising:providing a customer profile that stores data related to the firstcustomer; updating the customer profile via performing a firstsubprocess to collect interaction data related to the interactionsbetween the first customer and the contact centers, the first subprocessbeing performed repetitively so to update the customer profile aftereach successive one of the interactions, wherein, described in relationto an exemplary first one of the interactions (hereinafter “firstinteraction”) between the first customer and a first one of the contactcenters (hereinafter “first contact center”), the first subprocessincludes the steps of: monitoring activity on the communication deviceand, therefrom, detecting the first interaction with the first contactcenter; identifying data relating to the first interaction forcollecting as the interaction data; and updating the customer profile toinclude the interaction data identified from the first interaction;generating an interaction predictor, the interaction predictorcomprising knowledge about the first customer derived, at least in part,from the data stored within the customer profile, the knowledgecomprising a behavioral factor attributable to the first customer givena first type of interaction (hereinafter “first interaction type”); andaugmenting the customer profile by storing therein the interactionpredictor, the storage of the interaction predictor linking thebehavioral factor to the first interaction type to facilitate real timeretrieval when detecting an incoming interaction of the firstinteraction type.
 2. The computer-implemented method according to claim1, further comprising the steps of: detecting the incoming interactionthat comprises the first interaction type; in response to detecting thefirst interaction type, retrieving the interaction predictor andidentifying the behavioral factor corresponding thereto; and modifying,in accordance with the behavioral factor, a manner in which the servicesare delivered to the first customer in the incoming interaction.
 3. Thecomputer-implemented method according to claim 1, further comprising thesteps of: detecting the incoming interaction that comprises the firstinteraction type; in response to detecting the first interaction type,retrieving the interaction predictor and identifying the behavioralfactor corresponding thereto; and transmitting the behavioral factor toa one of the contact centers involved in the incoming interaction foruse thereby in modifying a manner in which the services are delivered tothe first customer in the incoming interaction; wherein the behavioralfactor of the interaction predictor is defined as an emotional state,behavioral tendency, or preference attributable to the first customergiven the first interaction type, and the machine learning algorithmcomprises a neural network.
 4. The computer-implemented method accordingto claim 2, wherein the contact centers involved in the interactionsfrom which the interaction data was collected include multiple differentones of the contact centers; wherein a plurality of the steps of thefirst subprocess is performed by an automated assistant application(hereinafter “automated assistant”) operating on the communicationdevice of the first customer; wherein the customer profile comprises oneor more cloud-hosted databases that are updated, in accordance with thefirst subprocess, via the automated assistant transmitting the collectedinteraction data over a network to the one or more cloud-hosteddatabases.
 5. The computer-implemented method according to claim 2,wherein the interaction predictor is generated by a second subprocessthat includes the steps of: after the customer profile is updated by acompleted iteration of the first subprocess, identifying a dataset thatincludes the interaction data stored in the customer profile; andderiving the knowledge of the interaction predictor by applying amachine learning algorithm to the dataset to identify patterns thereincorrelating one or more input factors to one or more outcomes relevantto the first customer given the first interaction type.
 6. Thecomputer-implemented method according to claim 5, wherein the behavioralfactor of the interaction predictor comprises an emotional stateattributable to the first customer given the first interaction type, theemotional state described by at least one descriptor representative ofeither a negative emotional state or a positive emotional state.
 7. Thecomputer-implemented method according to claim 6, wherein theinteraction data included in the dataset comprises data from theinteractions evidencing the negative and positive emotional states,including at least one of the following: feedback data comprising datarelated to an evaluation, survey, or satisfaction score provided by thefirst customer after a termination of the interaction; and conclusorystatement data comprising data related to statements made by the firstcustomer as the interaction is concluding, wherein the meaning of thestatements is extracted by natural language processing.
 8. Thecomputer-implemented method according to claim 6, wherein the firstinteraction type is defined as ones of the interactions having aparticular intent such that: the interaction predictor comprises acustomer-specific prediction relating to the emotional state of thefirst customer for an incoming interaction having the particular intent.9. The computer-implemented method according to claim 6, wherein thefirst interaction type is defined as ones of the interactions involvinga particular one of the contact centers such that: the interactionpredictor comprises a customer-specific prediction relating to theemotional state of the first customer for an incoming interactioninvolving the particular one of the contact centers.
 10. Thecomputer-implemented method according to claim 9, wherein the secondsubprocess for generating the interaction predictors is repeated aftersuccessive iterations of the first subprocess until the customer profileincludes the interaction predictors predicting the emotional state ofthe first customer for incoming interactions involving each of thecontact centers.
 11. The computer-implemented method according to claim9, wherein a plurality of the steps of the first subprocess is performedby an automate assistant application (hereinafter “automated assistant”)operating on the communication device of the first customer; furthercomprising the step of: generating, by the automated assistant, one ormore user interfaces on a display of the communication device that: showthe emotional state of the interaction predictor related to theparticular one of the contact centers; and prompt the first customer forinput modifying the emotional state of the interaction predictor;receiving, by the automated assistant, input from the first customermodifying the emotional state of the interaction predictor; andupdating, by the automated assistant, the emotional state of theinteraction predictor in accordance with the input received from thefirst customer.
 12. The computer-implemented method according to claim5, wherein the behavioral factor of the interaction predictor comprisesan emotional state attributable to the first customer given the firstinteraction type, the emotional state comprising a severity rating thatrates a level of importance the first customer places on the firstinteraction type; and wherein the interaction data included in thedataset comprises data from the interactions evidencing the level ofimportance the first customer placed on the interaction, the level ofimportance being based, at least in part, on an analysis of aninteraction transcript in which usage of words indicative of a highlevel of emotionality and/or a low level of emotionality is evaluated.13. The computer-implemented method according to claim 12, wherein thefirst interaction type is defined as ones of the interactions having aparticular intent such that: the interaction predictor comprises acustomer-specific prediction relating to the severity rating the firstcustomer places on an incoming interaction having the particular intent.14. The computer-implemented method according to claim 5, wherein thebehavioral factor of the interaction predictor comprises an behavioraltendency attributable to the first customer given the first interactiontype, the behavioral tendency comprising an upselling/cross-sellingopportunity rating that rates a willingness of the first customer toconsider an upselling or cross-selling offer given the first interactiontype; and wherein the interaction data included in the dataset comprisesdata from the interactions describing at least one of: unsuccessfulupselling or cross-selling offers; successful upsell or cross-sellingoffers; or service or products purchased in relation to upselling orcross-selling offers.
 15. The computer-implemented method according toclaim 14, wherein the first interaction type is defined as ones of theinteractions having a particular intent such that: the interactionpredictor comprises a customer-specific prediction relating to theupselling/cross-selling opportunity rating for the first customer for anincoming interaction having the particular intent.
 16. Thecomputer-implemented method according to claim 5, wherein the behavioralfactor of the interaction predictor comprises an agent preferenceattributable to the first customer given the first interaction type, theagent preference comprising a preferred agent characteristic for thefirst customer given the first interaction type; and wherein theinteraction data included in the dataset comprises choice data, thechoice data comprising preferred agent characteristics of the firstcustomer based on manual selections the first customer previously madewhen prompted to manually select an agent from among a plurality ofoffered agents.
 17. The computer-implemented method according to claim16, wherein the first interaction type is defined as ones of theinteractions having a particular intent such that: the interactionpredictor comprises a customer-specific prediction relating to thepreferred agent characteristic for the first customer for an incominginteraction having the particular intent.
 18. The computer-implementedmethod according to claim 2, further comprising the step of: providingone or more customer databases storing data related to other customers,wherein the one or more customer databases include interaction datarelating to interactions occurring between the other customers and thecontact centers; and wherein the interaction predictor is generated by asecond subprocess that includes the steps of: identifying a dataset thatincludes the interaction data stored within the one or more customerdatabases; deriving the knowledge of the interaction predictor byapplying a machine learning algorithm to the dataset to identifypatterns therein correlating one or more input factors to one or moreoutcomes relevant to a type of customer given the first interactiontype; and attributing the interaction predictor to the first customerbased shared similarities between the first customer and the type ofcustomer.
 19. The computer-implemented method according to claim 18,wherein the type of customer represents a subgroup of the othercustomers having one or more common characteristics found to bepredictively relevant by the machine learning algorithm in regard to theinteraction predictor; and wherein the step of attributing theinteraction predictor to the first customer comprises: after thecustomer profile is updated by a completed iteration of the firstsubprocess, identifying data within the customer profile relevant to theone or more common characteristics; and confirming that the one or morecommon characteristics are possessed by the customer.
 20. Thecomputer-implemented method according to claim 19, wherein the customerprofile and the one or more customer databases store biographicalpersonal data relating to the first customer and the other customer,respectively; wherein the one or more common characteristics relate toone or more respective characteristics stored within the biographicalpersonal data; wherein the behavioral factor of the interactionpredictor is defined as an emotional state, behavioral tendency, orpreference, and the machine learning algorithm comprises a neuralnetwork.
 21. The computer-implemented method according to claim 20,wherein the behavioral factor of the interaction predictor comprises anemotional state attributable to the type of customer given the firstinteraction type, the emotional state described by at least onedescriptor representative of either a negative emotional state or apositive emotional state; wherein the interaction data included in thedataset comprises data from the interactions evidencing the negative andpositive emotional states, including at least one of the following:feedback data comprising data related to an evaluation, survey, orsatisfaction score provided by a particular one of the other customersafter a termination of the interaction; and conclusory statement datacomprising data related to statements made a particular one of the othercustomers as the interaction is concluding, wherein the meaning of thestatements is extracted by natural language processing.
 22. Thecomputer-implemented method according to claim 21, wherein the firstinteraction type is defined as ones of the interactions having aparticular intent such that, once attributed to the first customer: theinteraction predictor comprises a customer-specific prediction relatingto the emotional state of the first customer for an incoming interactionhaving the particular intent.
 23. The computer-implemented methodaccording to claim 21, wherein the first interaction type is defined asones of the interactions having a particular one of the contact centerssuch that, once attributed to the first customer: the interactionpredictor comprises a customer-specific prediction relating to theemotional state of the first customer for an incoming interactioninvolving the particular one of the contact centers.
 24. A system forpersonalizing a delivery of services to a first customer, the firstcustomer having a communication device through which the first customerconducts interactions with contact centers, the system comprising: ahardware processor; and a machine-readable storage medium on which isstored instructions that cause the hardware processor to execute aprocess, wherein the process comprises the steps of: providing acustomer profile that stores data related to the first customer;updating the customer profile via performing a first subprocess tocollect interaction data related to the interactions between the firstcustomer and the contact centers, the first subprocess being performedrepetitively so to update the customer profile after each successive oneof the interactions, wherein, described in relation to an exemplaryfirst one of the interactions (hereinafter “first interaction”) betweenthe first customer and a first one of the contact centers (hereinafter“first contact center”), the first subprocess includes the steps of:monitoring activity on the communication device and, therefrom,detecting the first interaction with the first contact center;identifying data relating to the first interaction for collecting as theinteraction data; and updating the customer profile to include theinteraction data identified from the first interaction; generating aninteraction predictor, the interaction predictor comprising knowledgeabout the first customer derived, at least in part, from the data storedwithin the customer profile, the knowledge comprising a behavioralfactor attributable to the first customer given a first type ofinteraction (hereinafter “first interaction type”); and augmenting thecustomer profile by storing therein the interaction predictor, thestorage of the interaction predictor linking the behavioral factor tothe first interaction type to facilitate real time retrieval whendetecting an incoming interaction of the first interaction type.
 25. Thesystem according to claim 24, wherein the process further comprises thesteps of: detecting the incoming interaction that comprises the firstinteraction type; in response to detecting the first interaction type,retrieving the interaction predictor and identifying the behavioralfactor corresponding thereto; and modifying, in accordance with thebehavioral factor, a manner in which the services are delivered to thefirst customer in the incoming interaction.
 26. The system according toclaim 24, wherein the process further comprises the steps of: detectingthe incoming interaction that comprises the first interaction type; inresponse to detecting the first interaction type, retrieving theinteraction predictor and identifying the behavioral factorcorresponding thereto; and transmitting the behavioral factor to a oneof the contact centers involved in the incoming interaction for usethereby in modifying a manner in which the services are delivered to thefirst customer in the incoming interaction; wherein the behavioralfactor of the interaction predictor is defined as an emotional state,behavioral tendency, or preference attributable to the first customergiven the first interaction type, and the machine learning algorithmcomprises a neural network.
 27. The system according to claim 25,wherein the contact centers involved in the interactions from which theinteraction data was collected include multiple different ones of thecontact centers; wherein a plurality of the steps of the firstsubprocess is performed by an automated assistant application(hereinafter “automated assistant”) operating on the communicationdevice of the first customer; wherein the customer profile comprises oneor more cloud-hosted databases that are updated, in accordance with thefirst subprocess, via the automated assistant transmitting the collectedinteraction data over a network to the one or more cloud-hosteddatabases.
 28. The system according to claim 25, wherein the interactionpredictor is generated by a second subprocess that includes the stepsof: after the customer profile is updated by a completed iteration ofthe first subprocess, identifying a dataset that includes theinteraction data stored in the customer profile; and deriving theknowledge of the interaction predictor by applying a machine learningalgorithm to the dataset to identify patterns therein correlating one ormore input factors to one or more outcomes relevant to the firstcustomer given the first interaction type; wherein the behavioral factorof the interaction predictor comprises an emotional state attributableto the first customer given the first interaction type, the emotionalstate described by at least one descriptor representative of either anegative emotional state or a positive emotional state.